Codeswitch

A Visionary Agenda—in Quilts, Mars, and Pound Cake

Artists and poets have unique ways to communicate salient truths about the human experience. In “Quilting the Black-Eyed Pea (We’re Going to Mars),” poet Nikki Giovanni conjures imagery that, on the surface, seems whimsical—space travel to Mars accompanied by the songs of Billie Holiday and slices of lemon pound cake. Yet her vivid language reminds us that we can learn from the past to imagine how we might construct our shared destiny on this planet, and on others. In one sense, her aim is practical: space exploration (and indeed all exploration) benefits from diverse perspectives. But on a metaphorical level, Giovanni expansively connects the past experiences of Black Americans to the future, which is “ours to take.” In the telling, she creates entirely new narratives of what interplanetary inclusivity could mean.

As with Giovanni’s poem, the artist Sanford Biggers revisits an American tradition of quilting and storytelling to create an imaginative bridge between the past and the future. In his work, the lives of Black Americans are an evolving and complex story with shifting meanings. Inspired by the idea that quilts may have provided coded information to African Americans navigating the Underground Railroad before the Civil War, Biggers adds new layers of information and meaning to the antique quilts. His work suggests that lessons learned through one of the darkest moments in American history can be reimagined, and, as in Giovanni’s poem, layered into a visionary agenda that embraces innovation and joy.

Over the last two decades, Biggers has been developing a singular body of work informed by African American history and traditions. Sanford Biggers: Codeswitch, the first survey of the artist’s quilt-based works, features nearly 50 pieces that seamlessly weave together references to contemporary art, urban culture, sacred geometry, the body, and American symbolism. The exhibition’s title refers both to the artist’s quilt series, known as the Codex series, and to the idea of code-switching, or shifting from one linguistic code to another depending on social situation.

Codeswitch is on display at the California African American Museum in Los Angeles from July 28, 2021, through January 23, 2022.

Text by J. D. Talasek

Images of works by Sanford Biggers courtesy of the California African American Museum.

Scientific Cooperation with China

Complexity and Visual Systems

Art, in both creation and experience, is one of the most complex of human endeavors. Artist Ellen K. Levy engages the mental loop of seeing, connecting, and processing by juxtaposing imagery that creates meaning from unexpected and often disconnected relationships. Printing, painting, and animating images of complex systems relating to society, biology, and economics, she creates visual contexts that critique technological progress gained at the cost of ignoring the importance of the environment and society.

For the past decade, Levy has incorporated renderings of US Patent Office drawings into digital collages made vivid with paint. About her latest series, Re-Inventions, she writes, “Most inventions are reinventions; they spin from developments in prior innovations. In my works I explore unintended consequences of technology and include (re)drafted plans of some of the patented inventions that cause them (e.g., steam engines leading to cumulative carbon dioxide emissions). Some of the patents propose remedies resulting from yet other (patented) technologies (e.g., protection from nuclear radiation).”

Levy, who is based in New York, has been exploring the interrelationships among art, science, and technology through her exhibitions, educational programs, publications, and curatorial work since the mid-1980s. As guest editor of Art Journal in 1996, she published the first widely distributed academic publication on contemporary art and the genetic code. With Charissa Terranova, she is coeditor of D’Arcy Wentworth Thompson’s Generative Influences in Art, Design, and Architecture (Bloomsbury Press, 2021), and with Barbara Larson, she is coeditor of the Routledge book series Science and the Arts since 1750. 

Levy’s work is a part of a group exhibition in Vienna titled EXTR-Activism: Decolonising Space Mining, curated by Saskia Vermeylen. More information about the exhibit can be found at https://www.wuk.at/en/events/extr-activism/.

All images courtesy of the artist.

Episode 5: Dinosaurs!

It may surprise you to learn that the enormous dinosaur skeletons that wow museum visitors were not assembled by paleontologists. The specialized and critical task of removing fossilized bones from surrounding rock, and then reconstructing the fragments into a specimen that a scientist can research or a member of the public can view, is the work of fossil preparators. Many of these preparators are volunteers without scientific credentials, working long hours to assemble the fossils on which scientific knowledge of the prehistoric world is built. In this episode we speak with social scientist and University of Virginia professor Caitlin Donahue Wylie, who takes us inside the paleontology lab to uncover a complex world of status hierarchies, glue controversies, phones that don’t work—and, potentially, a way to open up the scientific enterprise to far more people.

Recommended reading

• Read Caitlin Donahue Wylie’s article, “What Fossil Preparators Can Teach Us About More Inclusive Science.”
• Check out her book, Preparing Dinosaurs: The Work behind the Scenes, which is available for open access.

Transcript

Jason Lloyd: Welcome to The Ongoing Transformation, a podcast from Issues in Science and Technology. Issues is a quarterly journal published by the National Academies of Sciences, Engineering, and Medicine and Arizona State University. I’m Jason Lloyd, the managing editor of Issues. On this episode, I’m talking with Caitlin Wylie. She’s an assistant professor of science, technology, and society at the University of Virginia. She wrote an essay for the fall 2021 Issue called “What Fossil Preparators Can Teach Us About More Inclusive Science.” And she recently wrote a book, Preparing Dinosaurs: The Work Behind the Scenes, published by MIT Press, which is about the workers in fossil preparation labs and their often unacknowledged contributions to science. So Caitlin, thank you very much for joining us today on our podcast.

Caitlin Wylie: My pleasure. Thanks for having me.

Lloyd: You wrote a fantastic piece for the fall Issue about preparing dinosaurs and what the role of fossil preparators is in paleontology research. So I thought a good place to start might be if you could talk a little bit about Keith, who’s one of the fossil preparators you describe in your essay.

Wylie: Yeah, thanks. So Keith—and that’s not his name, that’s a pseudonym—he is a volunteer at a museum and he works in the fossil prep lab. And he’s a pretty typical volunteer in the sense that he’s retired. He was self-employed, so he ran a business for most of his career. He’s a veteran. And for him, the point of being in a lab was really to be able to contribute something back to society—which is interesting, because usually when we retire, we think we’ve done enough for society. But he was really attached to the idea that he was serving science by preparing fossils. And of course, volunteering in a lab is different from volunteering as a museum docent, say, or at a soup kitchen, because the work of preparing fossils is really skillful. So Keith had to invest a lot of time learning how to work with these specimens under the guidance of more experienced preparators. And once he put the time in, he was showing up every day, sometimes putting in a full work day just because he found it so satisfying and rewarding.

Lloyd: I’m curious how Keith got interested in this job. Was he really into paleontology? Was he a frequent museum goer? What was the initial reason that he did this in retirement?

Wylie: It’s interesting because a lot of volunteers say, “I’ve always loved dinosaurs. I’ve loved dinosaurs since I was a kid.” But Keith was unusual in that he did not love dinosaurs. He wasn’t all that interested in dinosaurs. But he was very good with his hands. He really liked doing home improvement projects. He was into carpentry as a hobby, and he liked the idea of working in a lab where the tools are tools that he’s familiar with: basic hammers and chisels and little drills. And he found that setting very familiar and comforting.

So for him, it wasn’t so much about the dinosaurs as about the work itself. He often said that he found it relaxing. He was scheduled to come like, I don’t know, Mondays and Wednesdays every week. And then sometimes he would show up on a Thursday and the staff preparators would say, “Hey Keith, what are you doing here?” And he would say, “Well, I did all of my chores at home and I ran out of things to do, so here I am.” But for him it was really a place to go. He had a lot of friends among the other volunteers. He loved to talk to the staff preparators. So for him, the community aspect was strong and the tools were something that he loved, but not so much the science, which is interesting.

Lloyd: So what did he do as a volunteer preparator? What was he doing?

Wylie: Yeah. So the staff preparators would assign volunteer preparators a bone, and that would be their bone until it was finished. And that’s a really important way of training volunteers, that they really do a bone from start to finish and see all of the steps along the way. So I followed Keith because I was following the bone that he was working on, which was a vertebra of a hadrosaur, which is like the cow of the dinosaur era. They’re pretty common, and the vertebra he was working on was broken into several pieces. So basically he was handed this field jacket, and he had to dig the rock out and find out where the fossil was in all of this wrapping that they’ve put around it to protect it on the journey to the museum.

And then once he did that, he put it under a microscope and used what’s called an air scribe, it’s pneumatic, an air-powered sort of hammer and chisel type thing. It’s basically a little drill, handheld, to get the rest of the rock off the bone so that you could see the surface. And then he moved into a reconstruction phase where he had all these bits of dinosaur bone, and he was trying to piece them together to make the vertebra look more like it would in life. For that, he used a variety of different glues and adhesives—which is something that fossil preparators care about a lot. Glue is absolutely central to vertebra paleontology because the specimens are always fragmentary because of the process of fossilization.

You can’t really follow a bone without following the person who’s working on it. So I sat next to Keith for a lot of hours as he was chipping the rock off or trying to piece bits together and telling me what he’s working on. He would narrate while I was watching.

Lloyd: When he gets the jacket and starts opening it up and trying to find the fossil inside, is it fairly clear what’s bone and what’s not, or is that part of the skillset of the preparator?

Wylie: That is a crucial skillset of the preparator. So no, it’s often not at all clear what is fossil and what is rock. Because, of course, the fossil and the rock, the fossil is rock. They’re made of the same minerals. The bone has been replaced with minerals over millions of years. And so they are, materially, often identical. Sometimes there’s a difference in color, which is very helpful. Sometimes there’s a slight difference in texture. And of course, bones are porous when rocks are usually not. So if you see little bumps or little holes in the specimen, you know that’s bone and you’ve gone too far because you’ve penetrated into the inner bone instead of the surface.

Usually to become a preparator, you have to pass what they call the “prep test,” where you walk into the lab as an applicant and they hand do a crappy fossil, usually a fish or something that museums have a lot of and is not very scientifically important. And they hand you a tool and they say, “Take the rock off,” with no training. And if you pass that—basically if you don’t damage the fossil—then they’ll take you on and train you. And they think that that prep test is testing for certain innate skills that you have to have to be a fossil preparator that you cannot learn. Isn’t that fascinating?

Lloyd: Yeah. That’s really interesting. What are those innate things that they think this tests for?

Wylie: Attention to detail, manual dexterity, so like fine motor skills, and patience. Are you willing to work really slowly? And basically, if the applicant passes, then they start the process of training with the staff preparators, where basically they just get another fossil to work on and the staff preparator checks in on them a bunch to see how they’re doing and give them advice. So for example, I’ve seen preparators use a Sharpie to mark the rock, to show the volunteer what to remove, to show them the distinction between the fossil and the bone. And through instruction like that and through lots of time spent staring at these materials, that’s how volunteers learn how to distinguish fossil from rock.

Lloyd: You mentioned adhesives before, and those sound extremely important for reconstructing a fossil. And one of the things that I found really interesting—I don’t think you mentioned it in the essay, but you do talk about it a bit in your book—is that different institutions and different places have different cultures around the kind of adhesive they use and whether they use it or not. Could you talk a little bit about that? This gets to maybe how you got interested in this subject when you were studying abroad in the United Kingdom, right?

Wylie: Totally. Yeah, thanks. The cyanoacrylate controversy was a massive disagreement—continues to be a massive disagreement among the community of fossil preparators. Cyanoacrylate is the chemical name for super glue. It bonds two bones together in an instant, and then you can’t take it off, you can’t dissolve it. And some preparators think that’s great, because it’s really strong and it works fast. You don’t have to wait for it. And their thought is, “I’ve put this piece together perfectly. Why would anyone ever want to take it apart?” So that’s the camp that really believes strongly in cyanoacrylate. It tends to be a more American-heavy camp. And the opposing view is the idea, borrowed from conservators, that all materials put on a specimen and should be removable. The conservation-minded camp argues for adhesives that are basically based on solvents, so you can re-dissolve them if you wanted to take them off and actually remove the chemicals altogether. But those take a long time because for them to adhere, the solvent has to evaporate. So you have to sit there and hold the bits of bone together in a precise location while it dries. And it’s not as strong as cyanoacrylate.

I learned about those two opposing views because I was a student preparator at the University of Chicago, and I was taught to use cyanoacrylate—probably because I was working on not very important bones, right? I was a student, I was learning. And so probably no one ever will take those bones apart. And then as you said, I volunteered at the Natural History Museum in London for a semester. And they were, like, appalled that I asked for cyanoacrylate, and they introduced me to this other family of adhesives which are solvent-based.

I found them really hard to work with because they were so different from cyanoacrylate, in terms of how you line up the joints and how you apply them. In college, I just constantly had glue on my fingers. My fingers were just permanently super glued. And so for that not to be a part of fossil preparation, I struggled to learn that. That made me wonder, how can fossils be prepared in such different ways—and be compared and studied in the same ways? And none of that preparation work is documented in scientific papers. It’s beginning to be documented in specimen records, but even that is not universal. So it just blew my mind that the work of making a fossil researchable could be so different in the United States versus the United Kingdom and other places. And yet, the fossils are considered basically the same kinds of data.

Lloyd: Yeah. Actually, that really segues really well into the preparator’s role in research. So as the preparators work in putting together fossils, what role is that playing in paleontological research?

Wylie: So you might think that scientists would prepare their own specimens. And this is true in paleoanthropology, because there are so few fossils of human ancestors that scientists do prepare them themselves. But for vertebrate paleontology, there’s so many vertebrates compared to hominins that the bulk of it is just too much. It would take scientists forever to prepare the specimens and study them. It’s not sustainable in that sense, so they needed a division of labor.

The interesting thing about vertebrate paleontology is that the division of labor is so strong. Very few vertebrate paleontologists know how to prepare a fossil. They automatically take it to a preparator and preparators, the vast majority, have no idea how to study a fossil. I guess they would know what species they’re working on, but they don’t know how to identify species versus one species versus another. They don’t see that as relevant to the work they’re doing of revealing that specimen. And so that’s the part that I find really interesting, that that division is so strong even though they’re working on the same specimen. The handoff between fossil preparation and fossil research is a clean break. No pun intended.

Lloyd: Yeah. That’s really interesting. How do preparators talk about what they do?

Wylie: They say that they are serving science. They take a very long-term view, and they see that as their responsibility, to take a long-term view of the benefit, or the protection of, the specimen itself. They consider themselves advocates for the fossils, sometimes against their bosses. Scientists are the ones who hire staff preparators and pay for them through their grants or through institutional funds. So technically, the preparators work for the scientists. But preparators would say that they work for science in general. So not just this scientist who needs the fossil tomorrow to write a paper, they’re in a big hurry to publish or perish. Whereas preparators would say, “No, I need more time to prepare this fossil well,” or “I need more time to prepare it in a way that is conservation friendly so that the fossil lasts for another generation and isn’t just useful for you tomorrow.” So in some ways, fossil preparators are like the mediators between the specimens and the scientists, even though they’re all arguably working towards the same goal, which is learning about the distant past.

Lloyd: I’m interested in the disagreements that occur. I think you mentioned this in the book, that occasionally scientists will not allow a preparator to look at a jacket or an unprepared fossil yet, or vice versa. The preparator will keep a specimen from the scientist until he or she is ready to hand it off. Do those disagreements, those kinds of conflicts, occur frequently, or is that a pretty rare occasion?

Wylie: That’s a good question. The disagreements arise when preparators do research and when researchers do preparation. So there’s very much a territorial sense. That’s when they get upset. For example, in one lab, there was a scientist who would sneak into the lab at lunchtime when nobody was there and work on fossils. And he found it relaxing and just described it as like, “I just needed something brainless to do,” which is pretty offensive to the preparators.

And so the preparators installed locks on the specimen drawers so that he couldn’t get the specimens out, because he was hurting them. He was causing damage and, mostly, he was insulting the preparators by invading their territory. And the reverse happened too, in the sense that I talked to preparators who wanted to do research on fossils, like to write a paper describing a new species or comparing species, and their bosses, the scientists, would say, “No, that’s not your job.”

Some preparators got pretty dressed down by their bosses, yelled at. Others got fired for doing too much research. That wasn’t considered part of their job. And so the disagreements are very unequal in the sense that scientists have more power than preparators do. So preparators can’t fire our scientist for coming into the lab and breaking a specimen by trying to prepare it. But of course, scientists can fire a preparator. So yeah, they do a lot of work—both groups do a lot of work to distinguish themselves from each other, if you see what I mean. So yeah, that’s one way in which they do it is those conflicts.

Lloyd: Yeah. And that brainless comment made by the researcher sort of hints at the larger power dynamics. But just to get into that, how does the scientist conceive of what the preparator is doing? How do they describe what the volunteers are up to?

Wylie: Yeah. The difference in language here is really interesting. Often, scientists will say that preparators are cleaning fossils. And cleaning’s pretty easy, right? Anybody could wipe the dust off a countertop. And preparators never say that they’re cleaning fossils. They say that they’re preparing fossils, or sometimes they say that they’re sculpting fossils because those micro-decisions of “what is rock” and “what is fossil” feel like they’re sculpting or they’re creating. And those are really different ways of talking about this work, in the sense of the scientists kind of dismissing it as merely technical or grunt work that anybody could do, whereas preparators are likening it to art, which is much higher status than technical work.

Lloyd: That really gives you a sense of the hierarchies in the lab. So just to describe that hierarchy with some specificity, am I right in thinking that volunteer preparators are the lowest status and then there’s staff preparators and then it’s the research scientist, the paleontologists themselves? Is that sort of the general order?

Wylie: Officially, yeah. So in a museum, in the list of job titles and salaries, yeah, that’s the order. And sort of formal institutional power, yeah. In practice, it really depends on the context. For example, when fossils get broken—often by scientists who are trying to study them, and these fossils are super heavy and super fragile and they break under their own weight, so it might not even be a mishandling. But if a scientist breaks a fossil, it’s amazing how the power dynamic immediately shifts to the preparator. So then whatever the preparator says is going to happen. So the preparer might say, “I’m just going to glue this for you right now and give it back and you can keep working on it,” or the preparator might say, “You mishandled handled this. You can’t study it anymore.” Which is amazing, right? Very different from the formal hierarchy of the scientists having power over the technicians. So in case, preparators have that much power because they are the only ones who know how to fix that broken fossil.

The other case in which preparators have power is over the volunteers. So the scientists usually say, “The volunteers are not my problem.” And so it falls to the preparators to train them, select them, manage them as a workforce. And actually, that’s an incredible amount of power for technicians. And especially for technicians who don’t have standard credentials or a shared degree. In that sense, I think that volunteers are a major source of empowerment for preparators. And it also means that the preparators are in charge in the lab. Deciding what preparation methods to use totally falls to the preparators, scientists have no say in that. Partly as a power thing—preparators would never listen to a scientist who said, “You must use this tool,” because it’s not their expertise—and partly as a knowledge thing, that scientists really don’t know which tool to use. They wouldn’t know what recommendation to make.

So in that sense, preparators have a lot of power within their domain of the lab, over the other workers, volunteers, over how they’re going to prepare those specimens. And so then the power that scientist have really comes down to funding and what specimens the preparators are working on. So the scientists say, “I really want to study this bone. I need it in six months to write this paper.” And then the preparators do whatever they think is best to achieve that scientist’s goals.

Lloyd: So do the staff preparators, the paid preparators, do they have similarly varied backgrounds to the volunteer preparators, or do they have generally more a scientific background or a post-secondary degree in some sort of science?

Wylie: Yes. All of those things. Almost all preparators start as volunteers, which is interesting because the number of volunteers who become preparators is very small, percentage wise. But almost all of the staff preparators begin as volunteers and get that early training and exposure. Some of them have PhDs in paleontology, some of them have PhDs in literature. Some of them have only a high school education. It’s a really wide variety.

Lloyd: Does that very general hierarchy apply throughout paleontology, or do different institutions or maybe different kinds of institutions, such as museum labs versus university research labs, are there differences there? Or is it generally pretty much the same?

Wylie: It’s pretty much the same. I studied 14 labs in three countries. About half were in museums and about half were in university labs, and it seemed pretty much the same. The major difference was the number of workers. Generally, museums have a larger staff of everybody, more scientists, more preparators, more volunteers, whereas university research labs might only have one or two preparators. And they’re generally doing more specialized work. A scientist might only study fossil lizards, and then that lab’s only going to work on fossil lizards. The university ones tend to be more specialized in that sense.

There’s a slight difference in responsibilities towards the public. In universities, preparators work with grad students. Not necessarily to teach them how to prepare, but more to prepare specimens for them. And then in museums, preparators are somewhat responsible for the mission of the museum, as are all the staff, which is outreach and education. So they do a lot of lab tours. Training the volunteers is a form of outreach. I think if you work in a research lab, a university lab, you probably do less outreach and a little more work with students than in a museum lab. But yeah, those are the main differences.

And also, there are a couple of labs in museums that are for demonstration only. They’re not specifically research labs, and those are really different from taking a lab and just turning the walls into windows, which is the basis of most of the glass-walled labs that I studied. But there are a couple where they just have a couple of tools lying around, a couple of junky fossils. And they prepare them to show groups of school children, for example, as a demonstration, rather than actually preparing the specimens to be studied.

Lloyd: Oh, okay. That’s interesting. They’re like ersatz labs that are only for showing kids how it works.

Wylie: Yeah. So that’s more of a traditional display as opposed to an actual workplace that you can watch.

Lloyd: But there are some museum labs that are sort of fishbowl glass-walled labs, where the preparators are actually doing research and the public can see them doing what they’re doing.

Wylie: Totally. And I would say that’s the majority.

Lloyd: Okay. How do preparators feel? Do they like being on display in that sense, or are they annoyed by the attention, or do they just not really even think about it and get used to having people looking over their shoulder?

Wylie: It depends. A lot of the volunteers really like it. They like to be seen as someone who gets to work in a science lab. So they’ll wave at kids through the windows, or sometimes they’ll go outside and chat with visitors and stop working and take a break and serve as a public face of the lab. But staff preparators generally think it’s a drag, right? They’re in this business because they want to work with fossils. I don’t think I’ve ever met an extroverted fossil preparator. They really prefer the sort of solitary, focused work and they do outreach, like working in the glass-walled lab, as kind of a chore, as a service, but not as their favorite thing, for sure. And almost all museums that have glass-walled labs also have backstage labs, behind-the-scenes labs, and that’s usually where staff work.

And then it’s often the volunteers who are out in the public-facing lab. Part of that is because staff preparators are working on more complicated and more important fossils, so it’s just easier to do that in a place that’s quiet and has ideal air filtration and all the noisy tools that aren’t allowed on the museum floor, for example. I heard a couple of stories from labs that had been designed so that visitors and preparators could talk. So visitors could ask a question while a preparator’s working. And almost every single lab then removed that feature because it meant that the preparators were just answering questions all day long and not preparing fossils. And they found that infuriating. So yeah, lots of these labs have a telephone on the wall that doesn’t work or it used to connect into the lab.

Lloyd: It’s really fascinating that the most public-facing aspect of this research that occurs in museums would be enacted by these folks who are essentially members of the public themselves. They don’t necessarily have specialized scientific credentials, and they’re on a volunteer basis, and they’re the closest to the public. That just seems really interesting. Do they see themselves as sort of citizen scientists? That’s a very broad movement that comprises a lot of different sorts of research and people. But I wonder if they’re sort of enveloped in that broader movement to open up science a little bit more to the public.

Wylie: I think so. I don’t think they would identify as citizen scientists. They describe themselves as volunteers because most of them, like Keith, see themselves as distanced from the science. So they’re serving science, or they’re doing this work to help out scientists or to help out the museum, but they don’t see themselves as researchers. And most of them are like, “Why would I want to be a researcher?” They’re kind of dismissive of the very idea. And Keith would say things like, “I like working alongside people who are furthering the world of knowledge, and I’m just along for the ride.” And so there’s this sense of being science-adjacent that people like, rather than actually doing research.

Lloyd: That’s an interesting conception of their role. When the average, I don’t know if you studied this, when the average museum goer goes and sees this person working in a glass-walled lab, who do they think that person is? Do they think that that’s the paleontologist, or do they know what’s going on in the lab? Maybe this gets to why they were installing telephones that no longer work to ask them questions. Do you think the public has a sense of who these folks are and what they’re doing?

Wylie: No, they don’t. I thought a lot about what these labs are doing, because there are text panels around these labs and they say things like, “This is an air scribe, this is a microscope.” None of them, I never saw a sign that said, “These are volunteers. If you want to volunteer, take this flyer.” I never saw a recruitment form, I never saw any information about who these people were or what they were doing. So yes, I would stand outside the lab and eavesdrop on visitors to try to understand what they thought this was. And they would mostly say things like, “Look at the scientists” or, “Are those robots?”

Lloyd: Like at Disney World?

Wylie: Exactly, because that’s what you expect to see in a museum. You don’t expect to see people at work. And of course, fossil preparators don’t move very much. The movements they’re making are very small, so you can believe that it’s not a person, it’s a stuffed model or something. The conclusion I came to about the purpose of these labs is partly that they’re a scientific workplace. Volunteers are producing specimens that are going to be studied, in most cases. And the other function they serve I think is to show that a museum is a home of research. So we might think of museums as being a home of just dead stuff and finished facts written on these authoritative text panels. But actually, they’re housing a lot of research, and this is one way to show visitors this is a research lab. Research is a process, research is work, research is done by ordinary looking people wearing jeans and drinking coffee and chatting.

So it’s a pretty different front portrayal of science from the rest of a typical natural history museum. And the coolest part about it, I think, is that again, the text panels don’t really explain what the preparators are doing. They’re usually about the specimens or the tools, not about the people. And so I think that creates an opportunity for visitors to actually practice skills of scientific meaning making. You’re making observations, you’re trying to make sense of what you’re seeing. You’re asking yourself questions, “What are they doing? Who are these people?” And then you’re drawing conclusions. And that’s what scientists do. I think that’s what museums want to be teaching the public, is how to think like a scientist.

In that sense, these labs are very good for that because people don’t understand what’s going on. And the funny thing is that sometimes their conclusions are not what the preparators or the scientists would intend for their conclusions to be. I heard one woman approach one of these labs with a little kid and she said to the little kid, with great excitement, “Look, people making fossils.” So no scientist, no preparator would ever say they’re making a fossil. But yeah, you can understand why she got that idea, right? There’s plaster everywhere, there’s tools all over the place. You could totally understand why she would think that. And that’s drawing an evidence-based conclusion.

Lloyd: I did not realize that about the signs around the glass-walled labs, that they just don’t even mention who’s in there. It’s just maybe the tools that they’re using. But that does get to one of the things that you talk about where the preparators get very little, if any, credit for their work anywhere. And one of the things you talk about is potentially doing, in order to give credit, potentially provide some authorship to papers or to research papers that the scientists have written or, even just acknowledging them in the methods section. And I was wondering, is that a moral stance, or would that have some effect on the research itself or the products of research?

Wylie: Yeah, that’s a great questions. So I started this project from a Marxist perspective where I’m like, I’m going to go empower the proletariat, these oppressed workers who get no credit. And I very quickly abandoned that perspective because I realized how much power preparators actually have. They control the volunteer workforce. In effect, they control the space of the lab and the work that happens there and the decisions that go into each fossil. They choose their tools, they choose their materials. And so I started to think that actually being missing from scientific papers provides that space for preparators to have autonomy over their work and their workforce. And so I was thinking, there’s some evidence that as things become documented, as work becomes documented, then surveillance increases.

The classic example is nursing. 50 years ago, nurses pretty much did their own work because they were trusted as experts and professionals. And then as more and more documentation became common in the medical workplace, nurses lost some of that autonomy. So instead of saying, “I checked on the patient,” you had to document how you checked on it and what measurements you took, and so it lost that space for creative problem solving and judgment because it was becoming more documented. I worry that adding preparators to papers might increase scientists’ involvement in fossil prep decisions, which actually would be bad news for the preparators because that’s their main area of power. So I’m not sure that authorship is the right answer.

What I do think should be transparent is preparation methods. And preparators agree with me on this, and they really push each other to improve their documentation practices because it’s not a typical, traditional part of their work. For example, certain glues will screw up geochemical tests. So if you try to carbon date a fossil that has cyanoacrylate on it, it’s not going to work.

So that’s important to know for a scientist in 50 years who wants to date a particular specimen, and they have no idea what glue is in there—that’s going to impact what tests they can do. Keeping track of those kinds of materials and also who prepared it, because preparator skills are really different, and their decisions are really different, I think would be an awesome contribution to science, as part of the metadata of that specimen. And it would serve as a form of recognition, right? So if it’s in an institutional database of each specimen that includes the name of the preparator and all the materials they used and when it was prepared, that would make the preparator’s work look more legitimate, I think, more respectable, more scientific in a sense. But it would protect them from the surveillance that might come from being part of scientific papers.

Lloyd: Yeah. That’s really fascinating. I didn’t know that that would be a concern. It makes sense. And actually, that nursing example is helpful to extrapolate a little bit beyond the focus of your research: Are there positions comparable to fossil preparators in other fields? Nursing wouldn’t be one of them, they’re very specialized with a great deal of education. But I’m just thinking of other fields that may have people who come in on a volunteer basis, or maybe don’t have a scientific background, and do similarly very critical work for the research.

Wylie: Yeah. I’ve been thinking about this a lot. I would argue that the skill-based nature of research is ubiquitous, widespread. Even scientists have embodied skills of doing experiments, for example, that they don’t really describe. They get written out of papers that the rest of us outsiders wouldn’t know about. So the dependence of science on skill, I think, is ubiquitous. But the lack of credentials is really unusual in science. Lots of sciences have a history of lots of amateur participation—think of anything from natural history, botany, mycology, people who collect fungus as a hobby or people who study astronomy as a hobby. Those are very long histories of public involvement, but most of those fields have now specialized or credentialed those positions. And so now, if you like to look at the stars in your backyard, you’re not going to be considered a contributor to science; that’s your hobby.

I think preparators are not amateurs because they’re part of an institution. They’re working in a museum, they’re working in a research lab. Even if they’re volunteers, I would say they’re not amateurs because they’re not doing it on their own. I guess I would love for other scholars to tell me whether this position for people with a wider background, wider variety of backgrounds, exists in other fields, because I suspect that it does.

And one way which I know it does is with undergraduates. We all think that undergraduate research experience is a good thing for students. I’ve done a lot of research on undergraduate engineers, and I’m finding that it’s actually an excellent thing for the labs that these undergraduates work in, because undergrads bring this very interdisciplinary mindset that the grad students and the professors don’t tend to have, because they’re so much more specialized, right? They’ve had so much more education in engineering than the undergrads. So in that sense, the undergrads are kind of playing the role of preparators in the sense that they’re bringing in outside information, they’re having a different approach to problems that the professors think about in a very specific way.

Lloyd: So what would that look like, if there was a bigger focus on skills, maybe, rather than credentials, at different levels of the scientific enterprise, if you had to guess?

Wylie: I know, right. I’m a professor in an engineering school, so I hate to make this argument, but if we broaden paths to doing scientific work, that can only be a good thing. So I’m not arguing against STEM education, but education in science and engineering has a long history of discrimination and exclusion. I hope that we all will someday overcome that. That day is not today, it’s an ongoing process of making science and engineering education available to anybody. And so in the meantime, yeah, I think it would be awesome for science to include more kinds of people as volunteers, as technicians, as people watching from the outside—even that is a way of extending science beyond the lab. And I think this is good for people to participate in science, to learn that it’s not as elite and exclusive as it might seem, because that spreads scientific literacy. It spreads a sense of appreciation for science. It makes public trust in science stronger if people understand that science is just work done by people. It’s not magic.

And the other crucial thing it would do, I think, if we had a more diverse workforce in science, would be to bring ideas to scientists that are different. So to expose scientists to people who have backgrounds very different from theirs, which will bring in new skills that science, at the moment, doesn’t have or new ideas or new ways of understanding things that will improve the science for all of us. And crucially, watching the scientists chat with the preparators and chat with the volunteers, there’s a lot of knowledge exchange that happens just by having people around, hanging out together in the same space, talking about the same bone, people share a lot of knowledge.

And I think that that information sharing can help scientists learn to ask more relevant research questions. So for example, how can they use fossils to study how species adapt to climate change? Something that is crucial to our world now. How can they use fossils to study how environments change over time or change in response to rapid flooding, natural disasters, widespread wildfires, things that we’re experiencing that paleontology actually has enormous insights to offer? But I’m not sure those are the questions that scientists would come up with on their own. I think they need our help.

Lloyd: That’s a fantastic message for inspiring people to get more involved. So thank you for joining us for this episode of The Ongoing Transformation and thank you to our guest, Caitlin Wylie, for talking to us about the work fossil preparators do behind the scenes. Check out the show notes to find links to her Issues article, “What Fossil Preparators Can Teach Us About More Inclusive Science,” and to her book, Preparing Dinosaurs: The Work Behind the Scenes.

Please email us at [email protected] with any comments or suggestions. And if you enjoy conversations like this one, you should visit us at issues.org for many more discussions and articles. And I encourage you to subscribe to our print magazine, which is filled with incredible art, poetry, interviews, and in-depth articles. I’m Jason Lloyd, managing editor of Issues in Science and Technology. Thank you for joining us.

A New Compact for S&T Policy

Bridging Divides Through Science Diplomacy

The COVID-19 pandemic has presented the international community with a series of unprecedented scientific, social, and public policy challenges. Particularly in the early days of the pandemic, the world experienced a shift toward geopolitical tribalism exemplified by nationalistic quests for personal protective equipment, testing supplies, and therapies. Rhetoric focused on “self-reliance” cast a shadow beyond the political and into the scientific, further magnifying perceptions that science is a competitive rather than a collaborative endeavor and increasing concerns that such actions may be encouraging a retreat into research secrecy.

Nowhere has the retreat from international cooperation been more drastic and consequential than between the governments of China and the United States, where increasingly antagonistic dialogue has exacerbated existing tensions between the two countries. If continued, the growing geopolitical conflict between the United States and China and declining faith in multilateralism could dominate a post-COVID world.

We argue that it is critical to foster international cooperation in the face of global crises. Early-career researchers (ECRs) like us are in a unique position to create new and lasting ties among scientists, with implications for improved international relations and the progress of science more broadly. However, helping ECRs develop the necessary skills requires investment by both research institutions and governments.

COVID-19 has highlighted the importance of international cooperation in confronting global threats, which was observed through the role publications and knowledge exchanges played in quickly characterizing the virus. Collaboration appears to be an important way forward as the world looks to make meaningful progress in tackling both the pandemic and other pressing issues, such as climate change.

In mapping out his own experiences with “science for diplomacy,” President Obama’s science adviser John Holdren wrote that international science and technology (S&T) collaboration “foments personal relationships of mutual respect and trust across international boundaries that can bring unexpected dividends when the scientists and engineers involved end up in positions to play active roles in international diplomacy around issues with significant S&T content—e.g., climate change, nuclear arms control, and intellectual property.”

We define ECRs to include undergraduate and graduate students as well as those still in the early stages of their careers, in any sector. Precisely because they are early in their careers, ECRs are uniquely positioned to create bonds with foreign researchers now that can mature and strengthen over the coming decades.

ECRs are also key to collaborative efforts in low-risk research areas, which are nonpolitical and concern only basic scientific questions of mutual interests. Valerie Karplus, Granger Morgan, and David Victor noted in Issues that these “safe zones” could include research necessary to address climate change, such as advanced battery chemistry or carbon capture and sequestration, among other topics. These areas are unlikely to be of immediate commercial or military applications and thus are fertile grounds for developing international cooperative partnerships.

Looking back to another time of heightened geopolitical tensions—the Cold War—reveals that scientific cooperation at the level of individual laboratories, or through the exchange of students and scholars, was a popular and effective way of carrying out international cooperation. In the case of the United States and the Soviet Union, interpersonal relationships between scientists proved beneficial as the countries sought to cooperate on discrete space-related activities. Acknowledging the caveat that the political circumstances of the two periods are not identical, this type of approach, focusing on particular projects and individual relationships, could be used as a model to facilitate communication between China and the United States.

We also believe that any framework for scientific cooperation between the United States and other countries should center the role of ECRs. Areas of mutual interest present a prime opportunity for extending international collaboration beyond individual scientists to the level of research institutions and government agencies. Cooperation in such areas could be politically feasible despite geopolitical tensions: the 1985 Cold War-era agreement between the United States and the Soviet Union to jointly develop the International Thermonuclear Experimental Reactor (ITER), an international nuclear fusion facility, is an illustrative example. ITER continues to operate today with an expanded coalition of international partners aiming to develop nuclear fusion as a sustainable energy source. A more contemporary example is US-Russia collaboration on spaceflight programs. Although space cooperation between the United States and China is less likely in the face of political tensions, expanding cooperation in health security could present a more feasible opportunity to warm relations.

By actively contributing to these projects, ECRs can play crucial roles in developing research agendas as well as in building relationships with individual researchers. The interpersonal relationships that develop among ECRs over the course of cross-border collaborations could prove instrumental as these scientists rise through the professional ranks in diplomatic or research arenas. In his op-ed, Holdren credited the relationship he developed with the Soviet scientist Evgeny Velikhov during US-Soviet collaboration in the field of nuclear fusion with the success of the bilateral commission on the disposal of excess plutonium in the post-Soviet era.

Through this process of long-term relationship building, scientific cooperation at the level of individual scientists could play a central role in building trust between countries. Over time, countries involved in individual-level collaborations may become more amenable to broader collaborative efforts, even in the field of commercial technologies.

As an example of how early-career personal relationships can lead to cross-institutional and even cross-national trust, as well as far-reaching research progress, consider the relationship between Mark Levine and Zhou Dadi. Levine, director of the US-China Clean Energy Research Center (CERC) at the Department of Energy’s Lawrence Berkeley National Laboratory (LBNL), and Zhou, then an ECR in energy efficiency, began working closely in 1988 at the start of the LBNL initiative to support international clean energy research, development, and deployment. Twenty years later, by the time momentum was growing for a US-China agreement to address climate change, Zhou had become an advisor on energy issues to premier Wen Jiabao and director of the China Energy Research Institute.

Zhou and Levine’s relationship created a foundation for progress on climate-related research. It also fostered mutual trust in intellectual property protections, easing the way for more expansive agreements in the future. For instance, CERC developed an intellectual property protection plan that “may ultimately play an important role in building trust among the consortia participants, which could lead to even more constructive collaborations in the future, and serve as a model for future bilateral cooperation agreements,” according to a 2014 examination of the program. Thus, Zhou and Levine’s ECR relationship provided a powerful connection between the two countries that grew to support meaningful progress on the broader issue of climate change.

Although ECRs could be highly effective in making meaningful progress on a range of S&T issues, the lack of awareness about science diplomacy career pathways and dearth of training opportunities has inhibited their ability to participate in this arena. Thus far, science diplomacy has been largely taught to ECRs through extracurricular courses and workshops or within general science policy programs (see Table 1). But there is a clear case for increasing support for ECRs to receive science diplomacy training.

Universities and research institutions can play a crucial role by creating new science diplomacy courses or certificate programs and ensuring that students and scholars have opportunities to pursue work experience in science diplomacy and other policy-related fields. Workshops and seminars involving professionals working in these fields could help expose ECRs to the various available avenues and provide potential mentors.

We argue that science diplomacy should be taught as an elective course and included in career development discussions. One possibility is to build on existing virtual courses, such as those offered by S4D4C and the DiploFoundation, among others.

Informal communities and networks can also be valuable resources for researchers interested in learning more about science diplomacy, providing a platform for networking and opportunities for engagement. The National Science Policy Network (NSPN), where several of the authors met to collaborate on this article, is one such community. That community’s informal environment facilitates open dialogue and discussion of innovative solutions to confront global challenges. NSPN’s Science Diplomacy Exchange and Learning program (SciDEAL), which completed its inaugural year, facilitates collaborative work between ECRs and science diplomacy institutions, including nonprofit organizations, embassies, and consulates.

The move into a post-COVID world requires all hands on deck to build the international collaboration that will help science most effectively address pressing global issues. With additional training opportunities and mentorship, ECRs can play an even greater role in building trust between countries—a fact illustrated through recent historical examples. ECRs, including us, are looking to gain experience in cross-border cooperative projects now so that as we move along our career trajectories in academic and science policy spaces, we can help shape a policy environment that promotes science for diplomacy.

Table 1. Selected opportunities for early-career researchers to train in science diplomacy.

Episode 4: Art of a COVID Year

In the early days of the pandemic, communities began singing together over balconies, banging pans, and engaging in other forms of collective support, release, and creativity. Artists have also been creatively responding to this global event. In this episode, we explore how artists help us deal with a crisis such as COVID-19 by documenting, preserving, and helping us process our experiences. Over the course of 2020, San Francisco artist James Gouldthorpe created a visual journal starting at the very onset of the pandemic to record its personal, societal, and historical impacts. We spoke with Gouldthorpe and Dominic Montagu, a professor of epidemiology and biostatistics at the University of California, San Francisco.

Recommended reading

• See a selection of James Gouldthorpe’s artwork from the COVID Artifacts series.

Transcript

Host: Hello and welcome to The Ongoing Transformation, a podcast from Issues in Science and Technology, a quarterly journal published by the National Academies of Sciences, Engineering, and Medicine, and Arizona State University. You can find us at issues.org.

In this addition of the podcast, join J. D. Talasek, the director of Cultural Programs at the National Academy of Sciences, as he talks with artists James Gouldthorpe and epidemiologist Dominic Montagu about a series of paintings called COVID Artifacts.

J. D. Talasek: Hi everyone. I’m J. D. Talasek, and I’m the director of Cultural Programs at the National Academy of Sciences. Welcome to The Ongoing Transformation podcast. For over 10 years, my colleague Alana Quinn and I have had the privilege of working with the journal, Issues in Science and Technology. We get to suggest artists to feature in the magazine, and it has been a real joy to do so. We believe that not only do artists have a unique perspective, they also have a unique way of communicating that perspective.

For this episode, I’m joined by one of these artists, James Gouldthorpe, who is based in the San Francisco area. We’re also joined in discussion by Dominic Montagu, who is a professor of epidemiology and biostatistics at the University of California, San Francisco.

James, Dominic, welcome. We’re glad you’re here.

Gouldthorpe: Hello.

Montagu: Pleasure to be here.

Talasek: So I’d like to just start by asking you how you met. It sounds like the start of a bad joke: an artist and a scientist walk into a bar. So why don’t you tell us what the real story is? How did you guys meet?

Gouldthorpe: You want me to go, Dominic? It’s actually all-around parenting. Our sons, who are both now in their mid-twenties, met in middle school, and they and some other boys formed this really tight group of delinquents that have remained friends for many years now. And through them, we got to know each other as parents. Dominic’s home became the sanctuary for all these boys as they roam the streets. So, we always knew where they were when it came time to track them down.

Talasek: Well, it just reminds me—we talk about cross-disciplinary discussions and the way that different disciplines interact. What you just said reminds us that it’s because we’re all human and that we have other ways of connecting through just our systems of knowledge.

James, we reached out to you because of a body of work that you’ve done called COVID Artifacts. And I wonder if you could tell us about that project—how it started, maybe just describe it for us, as well as how you view it now, after a year or so?

Gouldthorpe: Like a lot of people at the beginning of the pandemic, there was a certain level of panic. We had been sent home. I actually work at SF MoMA, and we had been sent home with the idea that we were going to check in in two weeks and all come back to work at that point. And as we all know, it didn’t happen that way. So at home I was panicking, kind of spinning out, and I just retreated to my studio to start working. I don’t know if you remember back at the initial start of the pandemic, there was this video that went around of this nurse showing you how to disinfect your groceries. He took each one out, wiped it down. It was a bit excessive, but back then we didn’t know.

And I remember my wife and I did our first trip to the grocery store, a little local grocery store. And we came back and spent over an hour wiping down every item. It had started to occur to me the things that we had taken for granted, our regular daily items, had suddenly become this vector for death. We had no idea how dangerous these things were now. Suddenly, a bag of potato chips could kill you.

I got the urge to represent that somehow, so I sat down and I painted a bag of groceries, which now was weaponized. It was this terrifying thing that was part of our daily lives, but it had this feeling of danger around it. And I discovered that doing that, just staying in my studio, kept me from spinning out. It started to really help my mental health. So I began reviewing the daily news feeds, which got brutal. I mean, there are people who chose to look away from the news feeds. I did a deep dive and then every day I would try to find something new to represent in a painting.

Talasek: Dominic, I’m wondering if you can remember the first time that you saw this work that James was doing and what your response was initially to it?

Montagu: I didn’t see any of the painted until I went to see the show at SF MoMA. And then it was just extraordinary because at UCSF, we realized, I think, in January that something rather dramatic was going on in Asia, and the university started at having weekly updates tracking COVID-19. And the first cases—do you remember the boat that came into Oakland and they were identifying positive cases and sending them on airplanes to North Dakota? And Trump was saying, “It’s okay, there’s only 13 cases. So we think we’re fine. Look at Asia. China has 50 cases.” And I spent a year looking at the  and the infection numbers and from when it was single digits in the US and forecasting how bad it was going to get and worrying about that.

It was a really stressful year for all of the reasons that James said, as well. And I forgot all of the daily events. And I forgot the individuals. I forgot what it was like when that boat got towed through San Francisco Bay and those first few weeks, when we worried about groceries. The friends that I had who went to New York to support the doctors and nurses when New York seemed like it was overwhelmed and that was going to be the end of the world. And each of those episodes got replaced by a new trauma or by avoiding those traumas by focusing on the infection numbers and the statistics, or the mechanisms of infection, what we were learning. Is it aerated? Is it aerosol? Is it just droplets on objects that don’t absorb liquids? Do we only have to worry about droplets on metal? How well do we have to do all of this? Each new worry meant you had something to focus on that was pragmatic and you could control it a bit by understanding it and everything else got forgotten.

And so this was an amazing thing, to look at all of James’s paintings and have it all come just rushing back—both the human impacts that were so vivid in the moment and just returned, or even the impacts on all of us, remembering what it was like to get the first bag of groceries and “how worried should we be?” I remember hearing about people in Italy using bleach to wipe down every apple that they got and thinking, “We’re not doing that. Should we?” And yeah, it was incredibly impactful.

Talasek: Your account of that is almost exactly like mine. It was that I had forgotten that this happened. I had forgotten that we had experienced that. And I’m wondering also, Dominic, how does that feed back into your work and into your research? How does that inform a scientist, to have that sort of moment of reflection?

Montagu: A lot of epidemiology, a lot of biostatistics, is not thinking about individuals. We look at aggregate, we look at infection rates, at mortality rates per hundred thousand. And 600,000 people—we’re close to that [number of] deaths from COVID-19 in the US—I immediately want to think, “Well, but it’d be hundreds of thousands of people who would’ve died from other diseases if we didn’t have COVID.” I contextualize everything in abstracts. And so it’s quite powerful to have a collection of images that breaks you out of that and forces you to constantly think about the human context, the human importance that is behind all of the numbers. That, I think, matters—it’s why the numbers matter rather than the inverse. It’s not because there’s lots of people that we get excited by statistics. It’s the other way around. The statistics only have value because they represent people. And if you forget that, you lose an enormous amount. You’re doing things for the wrong reasons. So, it’s been very important to me.

Gouldthorpe: When I look back, particularly at the early works, they become these icons of human behavior in the face of near-apocalyptic events. And you see what becomes the focus. Suddenly we have a shortage of toilet paper, which never fully made sense to me. It was this sort of irrational response. And then as events went on, there was a period where I was like, “How am I going to keep painting these objects?”

But suddenly, society, social norms started to unravel. When the George Floyd murder happened, there was this explosion of protests and the exposing of just how deep [systemic] racism is. And then events just began to accelerate. Some people say to me that this project was a great idea, but it wasn’t really an idea. It was a reaction. I was just like, I want to stay ahead of this. I want to note how we behave, how we’re responding to this, and what layers are being exposed as we move along.

It’s interesting in retrospect, because even I forgot what some of the images were about. Things went by so fast. I was clicking and I was like, “I don’t know what that is, but it’s tragic.” I don’t want to forget, but then at the same time, it was so accelerated that, as a painter, I had a hard time maintaining the momentum, because it was so much happening.

And it keeps shifting. Out here in California, we ended up with the wildfires, and we had this apocalyptic sky that was very Blade Runner-esque that went on for a day. And then it seemed like the events grew larger and larger in their consequences as it went along, and it all seemed to stem from the pandemic. The pandemic seemed to be the foundation for this unraveling of society, I guess, is a very dramatic way to say it.

Talasek: I’ve heard you talk about your work, James, in terms of storytelling and in terms of narrative. And certainly what you’re describing here exactly fits into that larger impetus of your work. And I think that it also ties in with what Dominic was talking about. The work that he does in the lab is statistics and you’re dealing with numbers, but then the power of the narrative, such as is represented in your work, to humanize that and to connect that very necessary study of the numbers with what the numbers mean in our real lives.

I’m wondering, Dominic, in your work as a scientist, how does storytelling manifest itself for you? Once you crunch the numbers, so to speak, at what point is a narrative, like what James is creating, helpful?

Montagu: It becomes very important for communicating the visceral information that’s behind statistical reality, but it always works in the opposite direction of what James’s paintings have done. At least, as a scientist, you do the analysis, you look at the data, and then you identify stories that illustrate the data rather than being outliers to the data. You might have a great story, but it turns out it’s the one in a thousand where the person, they survived against all odds. Or they died, but not from the disease that you’re looking at; they got hit by a truck. And so it might be a great story, but you wouldn’t choose that because you’re choosing stories that illustrate data.

I think what James has done and why this resonated so strongly for me was it’s completely the opposite. It’s a collection of 365 and counting items of information, each of which is incredibly powerful. And the story is built from the collection of all of them. You don’t look at averages. You can see a shared narrative. In many ways, COVID turned us all sitting at home into observers of the world, much more so than we had been before. We would participate in real life more than somehow we did for a year.

And so, what you get is a story that is more like a reflection of real life, where it’s many different things which all built up to a collective influence. And I think you see that, and that doesn’t come out in data. Nobody analyzes data to produce that story. So, it’s been really interesting for me to try and think about the relative position, the relative utility of those different ways of approaching the creation of a narrative to reflect back something that’s happened.

I think that the paintings are really useful because they show a really complicated narrative and experience. I assume, James, that for any one person, 60% or 70% of the paintings will resonate with them. And the other ones—one of the paintings that I love the most is this enormous crowd of people on the Golden Gate Bridge. I don’t remember that. I never saw that. I really like it, but it doesn’t viscerally hit me. And yet there’s so much overlap between what you experienced and what I or anyone else experienced, that we build a bond there.

And the bond is much more interesting because it’s an imperfect overlap. If it was just the average experience, if it was just the statistically calculated median, it’d be much duller. It would reflect what all of us share, which is probably Trump and doctors in New York and three or four other things, but it wouldn’t be as nuanced and it wouldn’t be as powerful. What we didn’t both see is as interesting in this story as the things that we both saw on TV or in the newspaper.

Talasek: That’s an amazing description of what this is. And James, I’d like to get your response to what Dominic just said.

Montagu: Come on James, I want to hear you say, “I disagree completely.”

Gouldthorpe: I’m leaving right now [laughs]. One of the benefits of working at SF MoMA and having this exhibition at SF MoMA is I can go into the galleries and sit in the corner and basically loiter. I’m the doughy middle-aged guy in the corner that’s a little creepy. But I get to watch people as they review the year. The work up is not the entire year. It goes basically from the start of the pandemic until just post-election. There’s a lot of other work that’s not on the wall. And I can watch the recognition go across people’s faces. And something that I’ve been trying to do with my work over the past years is to create a communal event, in a way, when you come and visit my work, that you linger and you read it like a book or a painting.

And I’m pretty excited to see that people come to it and they’re all pointing at different paintings and sharing a story about it, sharing that moment. Or, “I don’t remember this, what was the…” Trying to get other people and they’ll gather and all discuss it and review it. And it’s humbling, for one thing, because I wasn’t thinking about that when I was painting them. I wasn’t thinking exhibition, I was just literally in my own head trying to get through the day. But I am witnessing this shared narrative, this global narrative.

It’s now this archive of the pandemic. Now, whether it’s going to be able to exist with as much intention after the pandemic’s over, I don’t know, because our memory’s going to fade even more. But at the moment it’s fresh enough that people are definitely finding a collective memory out of it. It’s interesting to watch, and it was unexpected. I’m really enjoying lurking in there and seeing how people respond.

Talasek: Well, it is interesting. James, do you see this as, or was it part of your original intent, for it to be such a healing process? I mean, you talk about it as originally just your needing to do something creative in response and then you see people coming together and you know that’s a healing conversation to have. Was that your original intention?

Gouldthorpe: It was not. I can’t claim there was any intention. The intention was really to keep my hand moving and my mind occupied. But then, I chose to use social media, which is something that I generally avoid. I don’t like the endorphin rush that you get addicted to with social media, but I decided to start posting daily. And as it went along, I started getting responses from people who were very appreciative of the work. A lot of frontline workers, when I would paint hospital scenarios and nurses and doctors, would write in their appreciation for my depiction. And over time, even the certain specific subject matter got in touch with me. I painted a young man getting arrested in St. Louis, and his girlfriend wrote and talked to me about that day.

And Rahul Dubey, who was the gentleman who gave sanctuary to the people in Washington [DC] during Trump’s little stroll over to the church—they were all about to be arrested for curfew. And he threw open his doors and he brought them all in and he had 70 people and they spent the night. And I did a portrait of him and he wrote me and now we’re in regular communication. His portrait’s on the wall and he was so excited that his portrait was in the museum.

Here’s the thing. I’m basically an introvert, so it was a little strange to have these strangers reaching out to me. But then that became my way to stay connected outside the studio. I was watching the news feeds, but to actually start to hear from people that were having the genuine experience that I was painting, exposed a reality that I was only experiencing through my laptop screen. When I was invited to do the exhibition, I was shocked, for one thing. But then, to be able to do this, to see people—and people still write me now and say, “I saw your show and it moved me in this way.”

It is unexpected. And I’m still processing what that means. I hope that it has a life beyond the exhibition and that I can continue to do work that has this meaning for people, because that’s really what I’m trying to find in my work. A lot of art deals with deeper conceptual things that have a limited audience that can begin to understand and to dissect that work. I like, if I can, that my work can actually have a human element that can reach people in unexpected ways.

Montagu: I have a question for you, James. Because of this forum, because you made the clear mistake to invite me on to also talk with you about this, when you’re painting in general, or with this series of paintings specifically, do you think about the differences between recording a lived experience and a scientific analysis of the world—whether that’s about something specific to diseases or physics or chemistry or other aspects of science—what do you think about what it means to have an artistic, or an artist’s perspective, on the world, versus the scientists and how those either are completely unrelated or how they complement each other?

Gouldthorpe: I think they’re very related. I think that if you go through contemporary art, you’ll find a lot of artists who are working very specifically within the sciences as well. And it’s interesting. On my behalf, I’ve long had an interest in science, and I’ve tried to work it into my art, but it’s not always been successful. I’m perhaps realizing that just because you have an interest doesn’t mean you have to make more about it.

I have seen artists who have been successful at it, but in the case of the pandemic, the science was so integrated to what was happening, that it was a crucial part of the narrative of the year. So in this case, I was able to review the science and represent it in the painting.

Now my other work, which is large narratives that I do, are fictionalized stories set in the past and the element of science is not there. I haven’t figured out a way to do that, that makes any kind of sense. Whenever I do it, it feels forced and inauthentic.

But if you start looking around and you look at artists, there are artists who work with scientists and do an amazing job bringing the two elements together. Not two elements, there’s multiple elements in this. I don’t think they’re very different, science and art. They’re both explorations of ideas. And I think that they both manifest crucial elements of our human existence. Wow, that was—I’m sorry. You can use that or not. That sounded ridiculous coming out of my mouth when I said it.

Montagu: One thing that this raised for me is, Why was there so little shared memory? Why was there so little, certainly none that I’ve seen, art that came out of the Spanish Flu of 1918? OK, in part because the news was shut down because of World War I, but this was immensely traumatic. And, there was very little scientific understanding of what was happening, little analysis of the disease that was helpful. And it also didn’t get shared or discussed.

And I wonder if those two things are related. That this was simply a shared trauma that had no explanation and no answer and that somehow that’s quite different from World War II. World War I, that produced… The answers and the resolutions came in the end of the war. And so, it became cathartic or useful to explore what happened in the war through art. And that somehow those two things relate: having a resolution through better understanding or through the conclusion of an event helps.

Talasek: It seems interesting to me, Dominic, going back to not having these communal experiences during the earlier catastrophes that you described. And a lot of creative outlets were probably lost. And I think that’s why it works such as what James is doing. It’s so very important. Around the same time that the pandemic hit, Cultural Programs of the National Academy of Sciences started collecting creative responses from artists, engineers, and scientists. Everyone was responding to it in different ways.

We started collecting those and that’s how we actually found out about James’s work. So thank you so much, James, because now it is part of the archive of our collective experience that will hopefully live on.

Talasek: Thank you for joining us for this episode of The Ongoing Transformation. I’d like to take a moment to thank our guests, James and Dominic. Thank you for taking your time to be with us, for sharing your insights on art, science, COVID-19, and our collective memory.

Visit us at issues.org for more conversations and articles. I’m J. D. Talasek, director of Cultural Programs of the National Academy of Sciences, signing off.

Retreating From Rising Waters

Time to Modernize Privacy Risk Assessment

In 2018, media reports revealed that a company called Cambridge Analytica had harvested data from millions of Facebook users, creating psychometric profiles and models that were then used for political manipulation. For Facebook, it was a high-profile privacy debacle. For the information technology and privacy communities, it was a particularly high-profile wake-up call.

This privacy failure was far too complex to be called simply a breach; it occurred across multiple layers and involved several companies. At the foundation of the scheme was data gleaned from Facebook’s “like” button, which Cambridge Analytica used to infer users’ personality traits. The company gained access to more people and more information through users’ profiles and Facebook friends (i.e., social networks). This unchecked flow of data was enabled by Facebook’s privacy policy, the way the platform interacted with third-party apps, and its desire to support social science research. Amazon’s Mechanical Turk, a platform for virtual paid piecework, also played a key role, as did various sources of public information, including US Census data. At first glance, the whole mess looks like a textbook example of an emergent property of a complex system: the interactions of multiple actors and systems producing completely unanticipated results.

It’s possible that Facebook didn’t see the potential for such a disaster brewing in advance because of outdated and inadequate methods for defining and evaluating privacy risks. Despite dizzying socio-technical changes over the past quarter of a century, organizations still rely heavily on assessments of privacy impacts with simplistic forms and functions that are poor matches for the layered complexity of today’s technologies. The United States is not alone in this; the data protection impact assessments required by the European Union’s General Data Protection Regulation, although an improvement in some respects, are similarly lacking.

As long as this dependence continues, we can expect new, more frequent, and ever-stranger privacy incidents. AI-based decisionmaking tools, for example, which often require large amounts of personal information for training their algorithms, can encode bias in their operation and injure or expose individuals to harm in everything from criminal justice proceedings to benefits eligibility determinations. The Internet of Things raises difficult issues of data aggregation—in which seemingly innocuous data points acquire much greater significance when combined—and of ubiquity, where multiple platforms can create mosaics of individuals’ activities. Biometrics, especially facial recognition, create additional potential for persistent surveillance as well as for problematic inference of individual attributes from physiological features. As these technologies develop, public- and private-sector organizations must update their approach to effectively manage privacy risk.

How we got here

In the early 1970s, the public was concerned about the potential implications of modern data processing systems, then standalone mainframes, for civil liberties. The US Department of Health, Education, and Welfare ordered a 1973 report by the Secretary’s Advisory Committee on Automated Personal Data Systems. The committee articulated a set of guidelines called the “Code of Fair Information Practices.”

That code formed the basis of the federal Privacy Act of 1974 and prompted the development of numerous, slightly varying, and expanded sets of best practices for protecting privacy. These practices—which typically included consideration of data collection, retention, and use as well as training, security, transparency, consent, access, and redress—were eventually dubbed Fair Information Practice Principles (FIPPs). Around the world, they became the de facto approach to protecting informational privacy. Most privacy statutes and regulations today are built on some version of FIPPs.

Chief among the approaches enabled by FIPPs are Privacy Impact Assessments (PIAs), which bear a name and constitute an approach partly inspired by environmental impact statements and assessments. Echoing these roots, a PIA is both the process of assessing a system’s privacy risks and the name of the statement that results. In the evolution of impact assessments, PIAs act as tools for addressing one particular societal value. However, they have been constructed in a way that renders them less about privacy as a human value and more about procedural niceties.

PIAs (and FIPPs) became further embedded in US law and practice when they were required for federal information systems by the E-Government Act of 2002. Today’s PIAs largely retain their original form—a set of written questions and answers about each of the FIPPs—and the same function, firmly rooted in identifying potential violations. Because FIPPs provide the principal structure of PIAs, they have become so intertwined with these processes and artifacts that they have together taken on a perception of inseparability. And as the interactions between society and computational technology become more complex, that perceived indivisibility increasingly poses problems.

Problems with the status quo

By using FIPPs to define privacy practices without requiring more expansive analysis, PIAs today maintain a relatively narrow and inelastic view of privacy. This static conception offers a very circumscribed model for imagining and understanding the risks that technological systems could pose to privacy. An ideal risk model describes possible threats, identifies vulnerabilities that might be exploited by them, and lays out what would happen if each exploit were realized, including its likelihood and severity. However, because FIPPs are the risk model utilized by PIAs, today’s consideration of privacy risks is largely restricted to violations of FIPPs. Furthermore, the close integration of PIAs and FIPPs, together with FIPPs-based compliance obligations, effectively discourages the use of other privacy risk models and assessment methods.

PIAs also suffer from two problems that have been significantly exacerbated by the evolution of technologies. First, PIAs tend to emphasize description over analysis, which prejudices them toward addressing privacy in a checklist fashion. Second, even when PIAs do explicitly invite discussion of possible privacy risks and potential mitigation strategies, risks are typically construed narrowly. They tend to be first-order problems, issues that might arise as the immediate result of system operation. Potential knock-on effects are seldom considered, nor are potential problems involving indirect cause and effect. 

These problems are compounded by the largely procedural nature of FIPPs. Consequences for individuals’ privacy are often framed only as possible FIPP violations—as violations of privacy-related procedure—rather than as violations of privacy per se. Many real results from privacy violations, such as embarrassment, lost opportunities, discrimination, physical danger (e.g., stalking), and more, are overlooked.

Another limitation of FIPPs is that they ignore the social context of systems, preventing analysts from considering potential harms originating in the external environment. Finally, FIPPs are so dependent on a system’s purpose, without carefully evaluating whether that purpose is fundamentally objectionable, that an unethical purpose can sometimes serve as the basis for satisfied FIPPs. If a system had the purpose of maintaining individual political dossiers on members of the general public, for example, a purely FIPPs-based analysis would take this as its unquestioned starting point and assess each principle relative to that disturbing purpose.

Other risk models and methods

Over the past two decades, other, more capable privacy risk models and assessment methods have been developed that could address the inadequacies of FIPPs and PIAs. Law professor Ryan Calo’s dichotomous privacy harms, for example, categorizes all privacy injuries as either subjective or objective, with the former forcing explicit consideration of potential impacts on individuals’ mental states—something often ignored by FIPPs-based models. Another model, a taxonomy of privacy developed by law professor Daniel J. Solove, proposes 16 different kinds of privacy problems divided into four groups relating to information collection, information processing, information dissemination, and invasions. This granular categorization enables more precise identification of privacy harms, again forcing more nuanced consideration of potential adverse privacy consequences.

Other risk models address vulnerabilities or threats. The contextual integrity heuristic, developed by information science professor Helen Nissenbaum, aims to identify violations of informational norms, which can be construed as privacy vulnerabilities. The model is noteworthy for explicitly recognizing the existence of social standards of privacy in various spheres of life, something FIPPs avoid by design. In contrast, frameworks such as LINDDUN, a privacy threat model and methodology, focus on modeling threats at the level of system architecture, considering factors such as potential attempts to link together system elements pertinent to individuals (data, processes, flows, etc.). Although situated at notably different levels, all these models attempt to discover issues that might ultimately affect privacy as experienced by individuals, rather than primarily looking for procedural problems.

Just as there are models beyond FIPPs, there are also new privacy risk assessment methodologies that could replace or complement PIAs. For example, the National Institute of Standards and Technology has developed a Privacy Risk Assessment Methodology that addresses systemic privacy vulnerabilities, defined as “problematic data actions,” and consequences, defined as “problems for individuals.” This methodology features numeric scores that explicitly estimate the likelihood and severity of privacy consequences. There are also more advanced quantitative options using statistical analysis, such as privacy expert R. Jason Cronk’s adaptation of the Factor Analysis for Information Risk framework, as well as a wholly qualitative but rigorous methodology called System-Theoretic Process Analysis for Privacy (STPA-Priv), which uses an approach originally developed to address the safety properties of system control structures. These models and methods have distinct emphases and orientations and could be mixed and matched for best effect.

Cambridge Analytica revisited

Could using other risk models and assessment methods have helped Facebook avert the Cambridge Analytica scandal? It’s not clear what, if any, privacy risk analysis was performed by the company, but it’s likely that more innovative approaches could have anticipated some of the factors that eventually led to the attempted political manipulation of Facebook users.

One fundamental reason Facebook users were vulnerable is that they were part of a social network. The privacy of any specific Facebook user depends in part on others in their network. This is, of course, part and parcel of being on Facebook, but that connectedness tends to be viewed exclusively as a feature—not a vulnerability. Cambridge Analytica exploited this weakness, termed “passthrough” by information science professors Solon Barocas and Karen Levy, in which the connections of one user enable access to the information of other users.

More recent risk models could have illuminated the threat of manipulation amid the tempestuous political climate of the time. Solove’s taxonomy, which considers decisional interference a significant privacy problem, might have suggested the potential consequence of inappropriately influencing voters. And if Facebook had performed an analysis using STPA-Priv, looking at the combination of technology and social context through the lens of hierarchical control and feedback loops, it might even have found the specific control failure scenarios that actually led to abuse. 

Using one or several of the models available, Facebook almost certainly could have identified and addressed at least some of the relevant control weaknesses, which might have prevented the debacle. These weaknesses included inadequate monitoring of researcher data use, insufficient restrictions on app access to user and friend profiles, and targeting of users across platforms. That apparently none of these weaknesses provoked concern at the time highlights the importance of adopting more capable approaches to privacy risk.

The complex role of technology in society demands that public and private entities expand their privacy risk assessment toolbox beyond FIPPs and PIAs. In the United States, the Federal Trade Commission should issue guidance for the private sector encouraging the adoption of a broader range of privacy risk models and assessment processes. The National Institute of Standards and Technology, through its privacy engineering program, should develop guidance and tools to assist organizations in comparing and selecting appropriate privacy risk models and assessment methods.

The White House Office of Management and Budget should update and supplement its existing PIA guidance for federal agencies, directing them to actively consider and deploy privacy risk models and assessment methods in addition to FIPPs and PIAs. Finally, the National Science Foundation should encourage and support research explicitly focused on enhancing privacy risk models and assessment methods, consistent with the 2016 National Privacy Research Strategy.

FIPPs and PIAs were innovative in their early days, but the world has changed dramatically. Modern technologies and systems require complementary and flexible approaches to privacy risk that are more likely to discover serious and unexpected issues. FIPPs and PIAs by themselves are no longer enough. Moving forward, organizations need to employ privacy risk assessments that ultimately serve the public interest.

Artificial Intelligence and Galileo’s Telescope

In 2018 Henry Kissinger published a remarkable essay in The Atlantic on artificial intelligence. At a time when most foreign policy experts interested in AI were laser-focused on the rise of China, Kissinger pointed to a different challenge. In “How The Enlightenment Ends” Kissinger warned that the Age of Reason may come crashing down as machines displace people with decisions we cannot comprehend and outcomes we cannot control. “We must expect AI to make mistakes faster—and of greater magnitude—than humans do,” he wrote.

This sentiment is nowhere to be found in The Age of AI: And Our Human Future, coauthored by Kissinger, Eric Schmidt, and Daniel Huttenlocher. If Kissinger’s entry into the AI world appeared surprising, Schmidt and Huttenlocher’s should not be. Schmidt, the former head of Google, has just wrapped up a two-year stint as chair of the National Security Commission on Artificial Intelligence. Huttenlocher is the inaugural dean of the College of Computing at the Massachusetts Institute of Technology.

The stories they tell in The Age of AI are familiar. AlphaZero defeated the reigning chess program in 2017 by teaching itself the game rather than incorporating the knowledge of grandmasters. Understanding the 3D structure of proteins, an enormously complex problem, was tackled by AI-driven protein folding which uncovered new molecular qualities that humans had not previously recognized. GPT-3, a natural language processor, produces text that is surprisingly humanlike. We are somewhere beyond the Turing test, the challenge to mimic human behaviour, and into a realm where machines produce results we do not fully understand and cannot replicate or prove. But the results are impressive.

Once past the recent successes of AI, a deep current of technological determinism underlies the authors’ views of the AI future and our place in that world. They state that the advance of AI is inevitable and warn that those who might oppose its development “merely cede the future to the element of humanity courageous enough to face the implications of its own inventiveness.” Given the choice, most readers will opt for Team Courage. And if there are any doubters, the authors warn there could be consequences. If the AI is better than a human at a given task, “failing to apply that AI … may appear increasingly as perverse or even negligent.” Early in the book, the authors suggest that military commanders might defer to the AI to sacrifice some number of citizens if a larger number can be saved, although later on they propose a more reasoned approach to strategic defense. Elsewhere, readers are instructed that “as AI can predict what is relevant to our lives,” the role of human reason will change—a dangerous invitation to disarm the human intellect.

The authors’ technological determinism, and their unquestioned assertion of inevitability, operates on several levels. The AI that will dominate our world, they assert, is of a particular form. “Since machine learning will drive AI for the foreseeable future, humans will remain unaware of what it is learning and how it knows what it has learned.” In an earlier AI world, systems could be tested and tweaked based on outcomes and human insight. If a chess program sacrificed pieces too freely, a few coefficients were adjusted, and the results could then be assessed. That process, by the way, is the essence of the scientific method: a constant testing of hypotheses based on the careful examination of data.

As the current AI world faces increasingly opaque systems, a debate rages over transparency and accountability—how to validate AI outputs when they cannot be replicated. The authors sidestep this important debate and propose licensing to validate proficiency, but a smart AI can evade compliance. Consider the well-known instances of systems designed to skirt regulation: Volkswagen hacked emissions testing by ensuring compliance while in testing mode but otherwise ignoring regulatory obligations, and Uber pulled a similar tactic with its Greyball tool, which used data collected from its app to circumvent authorities. Imagine the ability of a sophisticated AI system with access to extensive training data on enforcement actions concerning health, consumer safety, or environmental protection.

Determinism is also a handy technique to assume an outcome that could otherwise be contested. The authors write that with “the rise of AI, the definition of the human role, human aspiration, and human fulfillment will change.” In The Age of AI, the authors argue that people should simply accept, without explanation, an AI’s determination of the denial of credit, the loss of a job interview, or the determination that research is not worth pursuing. Parents who “want to push their children to succeed” are admonished not to limit access to AI. Elsewhere, those who reject AI are likened to the Amish and the Mennonites. But even they will be caught in The Matrix as AI’s reach, according to the authors, “may prove all but inescapable.” You will be assimilated.

The pro-AI bias is also reflected in the authors’ tour de table of Western philosophy. Making much of the German Enlightenment thinker Immanuel Kant’s description of the imprecision of human knowledge (from the Critique of Pure Reason), the authors suggest that the philosopher’s insight can prepare us for an era when AI has knowledge of a reality beyond our perception.

Kant certainly recognized the limitations of human knowledge, but in his “What is Enlightenment?” essay he also argued for the centrality of human reason. “Dare to know! (Sapere aude.) ‘Have the courage to use your own understanding’ is therefore the motto of the enlightenment,” he explained. Kant was particularly concerned about deferring to “guardians who imposed their judgment on others.” Reason, in all matters, is the basis of human freedom. It is difficult to imagine, as the authors of The Age of AI contend, that one of the most influential figures from the Age of Enlightenment would welcome a world dominated by opaque and unaccountable machines.

On this philosophical journey, we also confront a central teleological question: Should we adapt to AI or should AI adapt to us? On this point, the authors appear to side with the machines: “it is incumbent on societies across the globe to understand these changes so they reconcile them with their values, structures, and social contracts.” In fact, many governments have chosen a very different course, seeking to ensure that AI is aligned with human values, described in many national strategic plans as “trustworthy” and “human-centric” AI. As more countries around the world have engaged on this question, the expectation that AI aligns with human values has only increased.

A related question is whether the Age of AI, as presented by the authors, is a step forward beyond the Age of Reason or a step backward to an Age of Faith. Increasingly, we are asked by the AI priesthood to accept without questioning the Delphic predictions that their devices produce. Those who challenge these outcomes, a form of skepticism traditionally associated with innovation and progress, could now be considered heretics. This alignment of technology with the power of a reigning elite stands in sharp contrast to previous innovations, such as Galileo’s telescope, that challenged an existing order and carried forward human knowledge.

There is also an apologia that runs through much of the book, a purposeful decision to elide the hard problems that AI poses. Among the most widely discussed AI problems today is the replication of bias, the encoding of past discrimination in hiring, housing, medical care, and criminal sentencing. To the credit of many AI ethicists and the White House Office of Science and Technology Policy, considerable work is now underway to understand and correct this problem. Maybe the solution requires better data sets. Maybe it requires a closer examination of decision-making and the decisionmakers. Maybe it requires limiting the use of AI. Maybe it cannot be solved until larger social problems are addressed.

But for the authors, this central problem is not such a big deal. “Of course,” they write, “the problem of bias in technology is not limited to AI,” before going on to explain that the pulse oximeter, a (non-AI) medical device that estimates blood oxygen levels, has been found to overestimate oxygen saturation in dark-skinned individuals. If that example is too narrow, the authors encourage us to recognize that “bias besets all aspects of society.”

The authors also ignore a growing problem with internet search when they write that search is optimized to benefit the interests of the end-user. That description doesn’t fit the current business model that prioritizes advertising revenue, a company’s related products and services, and keeping the user on the website (or affiliated websites) for as long as possible. Traditional methods for organizing access to information, such as the Library of Congress Classification system, are transparent. The organizing system is known to the person providing information and the person seeking information. Knowledge is symmetric. AI-enabled search does not replicate that experience.

The book is not without warnings. On the issue of democratic deliberation, the authors warn that artificial intelligence will amplify disinformation and wisely admonish that AI speech should not be protected as part of democratic discourse. On this point, though, a more useful legal rule would impose transparency obligations to enable independent assessment, allowing us to distinguish bots from human speakers.

Toward the end of their journey through the Age of AI, the authors allow that some restrictions on AI may be necessary. They acknowledge the effort of the European Union to develop comprehensive legislation for AI, although Schmidt had previously criticized the EU initiative, most notably for the effort to make AI transparent.

Much has happened in the AI policy world in the three years since Kissinger warned that human society is unprepared for the rise of artificial intelligence. International organizations have moved to establish new legal norms for the governance of AI. The Organisation for Economic Co-operation and Development, made up of leading democratic nations, set out the OECD Principles on Artificial Intelligence in 2019. The G20 countries, which include Russia and China, backed similar guidelines in 2019. Earlier in 2021, the top human rights official at the United Nations, Michelle Bachelet, called for a prohibition on AI techniques that fail to comply with international human rights law. The UNESCO agency in November 2021 endorsed a comprehensive Recommendation on the Ethics of Artificial Intelligence that may actually limit the ability of China to go forward with its AI-enabled social credit system for evaluating—and disciplining—citizens based on their behavior and trustworthiness.

The more governments have studied the benefits as well as the risks of AI, the more they have supported these policy initiatives. That shouldn’t be surprising. One can be impressed by a world-class chess program and acknowledge advances in medical science, and still see that autonomous vehicles, opaque evaluations of employees and students, and the enormous energy requirements of datasets with trillions of elements will pose new challenges for society.

The United States has stood mostly on the sidelines as other nations define rules for the Age of AI. But “democratic values” has appeared repeatedly in the US formulation of AI policy as the Biden administration attempts to connect with European allies, and sharpen the contrast between AI policies that promote pluralism and open societies and those which concentrate the power of authoritarian governments. That is an important contribution for a leading democratic nation.

In his 2018 “How the Enlightenment Ends” essay, Kissinger seemed well aware of the threat AI posed to democratic institutions. Information overwhelms wisdom. Political leaders are deprived of opportunity to think or reflect on context. AI itself is unstable, he wrote, as “uncertainty and ambiguity are inherent in its results.” He outlined three areas of particular concern: AI may achieve unintended results; AI may alter human reasoning (“Do we want children to learn values through untethered algorithms?”); and AI may achieve results that cannot be explained (“Will AI’s decision making surpass the explanatory powers of human language and reason?”). Throughout human history, civilizations have created ways to explain the world around them, if not through reason, then through religion, ideology, or history. How do we exist in a world we are told we can never comprehend?            

Kissinger observed in 2018 that other countries have made it a priority to assess the human implications of AI and urged the establishment of a national commission in the United States to investigate these topics. His essay ended with another warning: “If we do not start this effort soon, before long we shall discover we started too late.” That work is still to be done.

Managing Retreat Equitably

Episode 3: Eternal Memory of the Facebook Mind

Social media and streaming platforms such as Facebook and Spotify analyze huge quantities of data from users before feeding selections back as personal “memories.” How do the algorithms select which content to turn into memories? And how does this feature affect the way we remember—and even what we think memory is? We spoke to David Beer, professor of sociology at the University of York, about how algorithms and classifications play an increasingly important role in producing and shaping what we remember about the past.

Recommended reading

• David Beer reviews Streaming Culture: Subscription Platforms and the Unending Consumption of Culture by David Arditi: “More and More and More Culture”
• Social Media and the Automatic Production of Memory: Classification, Ranking, and the Sorting of the Past by Ben Jacobsen and David Beer
• Spotify Wrapped, Spotify’s yearly wrap-up of your listening habits.

Transcript

Jason Lloyd: Welcome to The Ongoing Transformation, a podcast from Issues in Science and Technology. Issues is a quarterly journal published by the National Academies of Sciences, Engineering, and Medicine, and Arizona State University. I’m Jason Lloyd, the managing editor of Issues. On this episode, I’m talking to David Beer about social media and how these platforms, algorithms, and classifications play a role in shaping our memories and reality. David is a professor of sociology at the University of York. His new book, Social Media and the Automatic Production of Memory, which he co-authored with Ben Jacobsen, explores these themes.

Dave, thanks for joining us today. I just finished your book and it got my brain spinning in really terrific ways. You also recently reviewed David Arditi’s book Streaming Culture for Issues, which dealt with similar topics, and may be a good place to start. Could you tell me a little bit about “Chamber Psych?”

David Beer: Thanks. The “Chamber Psych” thing I started the review with, that was from Spotify, and it was the End of Year Review thing that they produce. It’s like an automated narrative of your music tastes that Spotify creates on your behalf and provides you with. It tells a bit of a story. And one of the aspects of the story is about genre.

So I’ve had this interest in genre as part of a broader interest in archiving—how media, new types of media platforms, [operate] through an archival lens. And then you start think how are they organized, what are the classifications that are going on within these spaces, and that sort of thing. They’re really interesting, the grids that we put culture into are really interesting and very vibrant, I think, as a result of the new types of media structures.

So my top five genres last year—I can’t remember now which number it was at, but in the top five was “Chamber Psych.” I didn’t know what that referred to, or which artist, which songs that referred to. It was a genre label I was unfamiliar with.

I found myself, as I mentioned in the review, searching for the genre that described my own taste so that I could understand how my tastes were being classified, really, or categorized. I thought that was interesting. I think the thing that drove it—I don’t mention this in the review—I think the thing that drove it was listening to the band Super Furry Animals and then a couple of other things that I’d listened to must have been categorized as that as well, I think.

Lloyd: And that’s how the platform slots those artists, or does it categorize them by album, or song?

Beer: Yeah, I think it’s by song. But I suspect it filters through from the artist. So the songs, I imagine, can be tagged with more than one classificatory label, but that seems to be the one. But I’ve been listening to Super [Furry] Animals for about 25 years and I’ve never heard of Chamber Psych.

It’s interesting how these labels then start to take on a presence within these platforms, even if they’re not perhaps labels that we might use ourselves. The other genres are more familiar, but it just struck me that it’s indicative of the vibrancy of the kind of classificatory systems that are going on in media structures.

Lloyd: I was really struck by the fact, I think you mentioned in the review, that Spotify has more than 2,000 different types of classifications for the various types of music that they host—Chamber Psych, obviously, being one of them. Could you talk a little bit more about the role of classification—obviously, on a streaming platform like Spotify, but also in other social media platforms such as Facebook?

Beer: Well, I think consumer culture is full of this kind of classificatory system. The comparable thing on Spotify you get on Netflix, you get on Amazon: these labels, very specific, granular types of labels that are used to organize content. I think this is driven, in part, by people’s involvement in the platforms, but it’s also to do with the amount of content that there is out there, and that needs to be organized.

We’re familiar with ideas about algorithms presenting content back to us, but I think alongside that, we sometimes focus less upon the classificatory systems, the kind of archival systems that are active. So once you get all this massive, massive content—like you get all these films, all these TV shows, all these podcasts, all these songs available, and you move to an access-type cultural consumption—you need ways of organizing that, so that it’s manageable. It needs to be rendered fathomable to a consumer, and one of the ways this works is through classificatory systems taking on a greater level of significance within the media structures, so that people can then find their way around and find culture they might be interested in.

So you get the automated thing presenting you with suggestions, but there’s also the classificatory structures that allow us to organize this content in different ways. You get that in the consumer-culture platforms like Spotify, Netflix, Amazon, and so on, but you also then get it within social media with people using hashtags and stuff to try to organize content within those spaces. There’s classificatory systems that work there as well, and you had it with tagging in the past—tagging photos and so on.

So these are classificatory systems. Some of them are user-generated classifications, others feed off that but are led by the platforms, and there’s this interesting mix there of everyday classifications that people use, combined with the classificatory schemas that are applied, or imposed, onto culture by the platforms themselves, or actors within them. It’s a quite interesting mixture, I think, of agendas going on within classification of culture and content. But behind it all is there’s so much there. You need ways, then, of managing it, and these are vast archives, as I see them as, of content that classificatory systems allow us to access and allow us to retrieve the things that we might be interested in.

Lloyd: I was really struck. I had not given this any thought, how a platform like Facebook takes a post and assesses it for how it’s going to classify it, then how it ranks it, and then how it determines whether or not it’s going to feed it back to you as a memory. I assume it has some sort of AI that looks at the image itself or classifies the image in some way, maybe fairly generally as “two people on a beach” or something like that, and then it also looks at how it’s tagged, the comments on it, I guess they do some linguistic analysis on whether those comments tend to be good or bad, and so the platform purports to have a sense of whether or not this post or this photograph will be positive or negative for the user. I had not thought about that.

Beer: And that’s where the interesting thing of these things is archival, and thinking about classifications. To understand the kind of politics of those archival structures, what they allow to be said, what comes back to us, what we see, what we encounter, then, are all a product of those archival structures and that kind of politics.

Memory, then, is a part of that. So that was the collaborative work I’d done with Ben Jacobsen, who has written widely about algorithmic memory as well. And, yes, we were trying to understand, in particular, the classification and ranking processes, and then how people responded to them. That’s the three movements in the book.

One of the starting points, really, was understanding that Facebook had a taxonomy of memories—types—and the content was then slotted into, literally, a grid. And, in the way that you’ve described, it’s assessed, and the images, the comments, and so on are used to place memories within a grid. … As we live in social media, they become memory devices. And that past content then becomes slots into these pigeon holes, these categories of types. And in that moment, you’re deciding, “Well, what, of that content we create, constitutes a memory?” And also then “What type of memory is it?” So at that moment, memory becomes part of the logic of social media. Because it’s the types of things that social media want us to engage with as memories—[those] are going to be the types things driven by the logic of stickiness, and sharing, and commenting, and engagement.

We thought that was quite interesting to see the grid. We use Facebook in this book, but what we were pointing towards is a broader set of trends in social media through memories: a memory culture. And we’ve also got mobile devices doing something similar, but we used Facebook as a way into that because we’d got this taxonomy. So we did that, yes. And then, once classified, they’re ranked for their worth or value, which is partly to do with the prediction of what the person will want to remember and when, and then we looked at how people respond to those recirculated and sorted versions of their own past.

Lloyd: You touched on this just now, but when I think about the goals of the platform, what this kind of classification scheme, the targeting and regurgitating of your memories, what the objective is for that, I’m thinking of other related concepts like James Scott’s ideas about legibility in the state: in order to get visibility and control over the population, they need a census, a sense of who lives there and where they are and what they do. In this case, it wouldn’t be for something like taxation or conscription—why is this useful for Facebook? What do they do with these systems?

Beer: Well, because people have been living on social media platforms and using social media platforms for a significant amount of time, we’ve built up a series of biographical traces about our lives within those platforms.Now, instantly then, if you run a platform and you want to have maximized personalization, then having people’s biographical traces is a significant, valuable resource for knowing them. It’s a way of knowing people through those pasts, and that becomes a resource for maintaining engagement in the present.

Obviously things like nostalgia, memories are powerful things for people and they’re understanding themselves, but also their understandings of their friendships and relationships, and connections with other people, collective understanding of what’s going on. So this can generate significant activity, because what you are looking to do is maintain people in that platform, as long as possible, each day.

That’s the objective, because, really, some of these social media platforms, they can’t grow much bigger in terms of the number of users. What they’re looking to grow is the level of engagement that people have with the platform, particularly as they’re competing more with each other, I suppose, a little bit on this now as well with generational shifts and so on.

It doesn’t have to be a long time in the past, it can just be a previous year or whatever, you don’t have to be recalling long periods of time for it to work, but it gets people engaging with their own past and with shared moments that then recirculate and trigger activity in the present.

Therefore it fills the gaps. It fills the voids within social media for us to say something, or respond, or act within the platform—to be active. So it gives us an option, little bit like memes, I think, they become these anchor points for activity that allow people to fill the spaces of social media and satisfy the obligation for activity, I suppose.

Lloyd: Yeah, and it seems really effective. Part of the book is about, as you mentioned, the user response to the memory feature. Could you talk a little bit about what you found? You did focus groups and some structured interviews, right?

Beer: So this was off Ben Jacobsen’s project. He’d performed his interviews, and has been writing about those, and this became a kind of side project to that, really, around classification and ranking. It was a kind of unexpected insight that came off the back of that. We started to think about ranking and classification and then how people were responding to their past content being classified and ranked. And we found some, we use Imogen Tyler’s term “classificatory struggles” within the space. So it’s not like these things fit seamlessly in. They do generate activity and they do generate content and stickiness, but they also create other outcomes. We detail this a bit in the book, but these are things to do with misunderstandings of memory. So, presenting back things that weren’t that significant to the individual.

That was one of the examples, one of the things we looked at. We also look at the way that, sometimes, it can feel invasive. It is part of the surveillance, I suppose it’s that creepy over-surveillance you sometimes get from these platforms that unsettles you.

And in other instances, it was almost like this reaction against the polished, sleek version of their past that didn’t feel quite right. It didn’t sit well with people to have their past packaged in quite such a neat way. That was one of the other things that we found. So people were engaging and showed that they were entertained and amused by these memories, or they saw it as useful in some instances, but there were also these struggles, uncertainties, unsettling properties to it as well, sometimes, that people found.

Lloyd: It strikes me as paradoxical that people would find these too polished, because the conventional wisdom about what you put on social media is that it’s your vacation photos and the studio pics of your baby and things like that. And so the idea that in a year’s time it would come back to you as a memory and you’d find it too sleek.

Beer: People use those types of terms in their response to it, but maybe part of this is that an automated story of your life can create a kind of unsettling presence or it can clash with your own version of your own past, and therefore feels wrong. Or it might just be that people have an uneasy sense of automation within the space.

We use Walter Benjamin in the book, there’s an illustration fragment from Walter Benjamin about how memories gain authenticity through the digging, through the actual digging them up, unearthing the memory actively, is how they gain legitimacy. And here, maybe it’s the fact that because people aren’t digging it for themselves when they’re presented back, there’s a sense that they lack authenticity, or lack a kind of legitimacy.

We problematize notions of authenticity in the book, but you can see how it might be communicated as a sense of not liking the kind of polished nature of what’s presented to them. That’s part of their response, perhaps, to automation.

Lloyd: That’s really interesting.So, you point to this in the book a bit as a potential path for future research, but I was wondering, if you were to speculate on what effect this automated production of memory, what these algorithms are doing, both to the individual and maybe to social relations more broadly—what do you think will be the effect?

Beer: It will change what we remember and how and when, because the things we’re encountering from our own pasts are coming up through the devices. So what we remember and how and when we remember it is going to be filtered through this archival structure. I think that’s already happening, that’s in place.

I think that there’s the potential there for a reworking of what the notion of a memory is. What we understand to be a memory could change as a result of this, that it is something that’s in the platform, as well, and that’s automated, and that is provided to us. And the selection of what a memory is by this system could then lead us to see memory through that lens, potentially. So there’s a possibility for that there. And then, I think, the third thing is that this will have consequences for individual notions of self, potentially, and identity, but also collective remembering.

I’m not sure we understand fully what the implications are for collective memory, and therefore for solidarity, social connections, and social divisions that could come from a transformation in the collective memory, when memory is something that’s personalized to algorithms and is fragmented at the level of the individual, potentially.

So I think there’s three things there: when we remember, what we remember, what we understand the memory to be, but also how individual and collective memory might operate in the future, particularly as these things become more and more embedded, more active, and, potentially, more predictive.

Lloyd: This type of feature seems to be everywhere. I assume it’s in part because they’ve just found it so effective, a really effective way of increasing and engagement. But it’s on your phone, Spotify now sort of famously has this year-end feature where they feed you back a memory of the year in music. So it doesn’t seem like it’s going away anytime soon.

Beer: I don’t think so. The first thing I did on the memory thing was maybe about four or five years ago, and you can just see it escalating. Most of these platforms and devices have got their version of presenting your past to you, or the automatic production of our past.

That seems to me to be spreading out into these platforms and devices, and they’ve just got more and more biographical traces on which to use, but also now the accumulation of data about people’s engagement with those recirculated memories, which then feeds into the system itself. So they can then use that to try to be predictive about which types of memories work and what to rank as being the memory to send back to you.

The consequence of that are difficult to predict really, because it might be that that narrows down memory, or it might be that they find that they want to try to create ways of being unpredictable, because that’s what people are. So you don’t know, but it’s going to get coded into the algorithms.

Lloyd: So you and your co-author on this book, Ben Jacobsen, did a really deep dive into this [memory] feature that is a fairly significant part of, but in some ways tangential to, the overall structure of the platform and what they try to do, which is increase engagement. So I’m wondering, what do you think about what social media is doing overall, after having looked at this particular feature that seems to have all these complexities and tensions in it, potentially a manipulative approach, although not necessarily—but it seems like this particular feature is such a rich source of research and tension. How does it make you think about the larger platform or social media itself?

Beer: Yeah, you’re right. This project is part of trying to build up a bigger picture around the way that these systems work, and what their objectives are, and what the politics of platforms is, and data, and algorithms, and that kind of thing. And I think you can understand this in terms of the broader transformation that we’ve seen through social media as it builds up.

I did another book called The Data Gaze, which is about how this gaze is exercised on us, how we’re watched through platforms and by data. So I think you can see the memory thing in terms of the broader political economy of platforms, and particularly social media, which is about the data.

The data of the archived users is really where the value is in social media. That’s where the predictions are, because the idea is you can use the data to be more predictive about individuals, and, therefore, target content towards them in ways where value can be extracted. So I think you can see the memory thing in that broad term.

So what you want to do is keep people engaging with the platforms as much as possible, because that generates the maximum amount of data about those individuals, which then lends itself value. Now I’m saying they can use the data to achieve the things they say they can achieve, but it’s the notion that that data is of value. The ideas around value attached to data are the really important things in terms of understanding the activities of a number of these platforms, I think. So you can see the memories thing, I think, through the broader ideas around data capitalism, probably.

Lloyd: And engagement.

Beer: Engagement really equals data production and stickiness. These are things that create, that increase the amount of data gathered, and therefore maximize the opportunities for value to be generated—or for notions of value to be generated, at least.

Lloyd: What’s been your experience with this feature on social media?

Beer: Apart from Spotify, which I think—I did the calculation—I think I spent 3.64% of 2020 on Spotify(you can work it out from the hours it gives you), I don’t actually have any social media profiles. And a student asked me about this recently actually, and I said, “Well, the reason I don’t have any social media platforms is because I do research them. I’m some sort of social media enthusiast.”

So I did my first project on social media in about 2006, 2007, I was working on a program called the e-Society program funded by the ESRC with a colleague called Roger Burrows. It was called Web 2.0 then, and I created a Facebook profile, and I found it very unsettling. So I deleted that after we had done a little bit of research on it.

And then I did have a Twitter account for about six, seven years, I deleted that. And the only thing I’ve really stuck with is blogging, really. Yeah. Blogs and working on blogs, used Medium for a bit, been using Substack, just experimenting with those types of mediums as a way of writing and communicating and being part of an online community. But it’s not quite the same thing, is it?

So personally, I never get presented with any memories about my past. There’s a problem. It’s “How do you understand social media from the outside?” is something that I’m always working with because I teach this as well. I actually find it’s quite useful, because you can look across platforms, internationally, to try to understand it, rather than being led by a targeted, personalized experience of the social media space, I think. That’s the way I justify it to myself, anyway. I have a kind of discomfort with social media, but I can see the value in social media and understand people’s engagement with it, absolutely do.

I see my job is to try to think skeptically about what’s going on and to try to think in sociological terms about broader transformations.

Lloyd: Thinking skeptically about the world and ongoing transformations is also our goal here at Issues. I’m grateful you joined us for this episode, Dave.

If you’d like to read more about the new process of digital memory making, check out his book, called Social Media and the Automatic Production of Memory, and visit us at issues.org for more conversations and articles. And of course, you can read Dave’s review there. I’m Jason Lloyd, managing editor at Issues. Thank you for joining us for this episode of The Ongoing Transformation.

Ethics in Animal Research

Digital Learning and Employment Records

Looking Beyond Economic Growth

Episode 2: Doing Science With Everyone at the Table

Could we create more knowledge by changing the way we do scientific research? We spoke with the NASA Psyche mission’s principal investigator and ASU Interplanetary Initiative vice president Lindy Elkins-Tanton about the limitations of “hero science,” and how she is using an inclusive model where collaborative teams pursue “profound and important questions.”

Recommended reading

Read Lindy Elkins-Tanton’s Issues essay, “Time to Say Goodbye to Our Heroes?

Transcript

Lisa Margonelli: Welcome to The Ongoing Transformation, a podcast from Issues in Science and Technology. Issues is a quarterly journal published by the National Academies of Sciences, Engineering, and Medicine, and Arizona State University. I’m Lisa Margonelli, editor-in-chief of Issues, and on this episode we’re interviewing Lindy Elkins-Tanton. Lindy is the vice president of the ASU Interplanetary Initiative, and she’s also principal investigator of the NASA Psyche mission, which launches in 2022 to explore a unique metallic asteroid orbiting the sun between Mars and Jupiter. In her August 2021 Issues essay, Lindy argues for a radical restructuring of how we do research, divesting from big names and asking teams instead to focus on big questions and ambitious goals. The future of humankind, she says, requires that we hear all the voices at the table—not only the loudest.

Margonelli: Lindy, thank you so much for joining us today. I’d like to ask you how you got interested in science. Was there some sort of ideal picture of what a scientist was?

Elkins-Tanton: That’s a great question. Thanks, Lisa. It’s really great to be here to talk about this. I’m looking forward to the conversation. You know, I often ask when I give a public lecture, especially to people interested in astronomy and planetary science: What was the moment in your life when you knew that you wanted to do this, or be interested in this, or follow this? It’s like the question you just asked me. And probably almost a third of the audience says it was when they saw Jupiter or Saturn through a telescope when they were 10 or 11 or 12. It’s just this formative moment. For the rest, it’s mostly Carl Sagan — Cosmos, Star Trek, Star Wars, and NASA. And I had all those things, including the Jupiter and Saturn sighting when I was about that age. But I still wanted to be a veterinarian.

I had this tremendous, all-consuming interest in natural sciences that carried me across all the disciplines. And even though as an undergraduate I studied science, I was not quite ready to go to graduate school. So for me, it’s been not a real direct path into science, but instead, a real passion that grew largely in the decade after my undergraduate degree in how teams of people work together. What is it that makes for not just a good outcome for the project, but a good outcome for the person? The thing that made me come back was the knowledge that in research science, the questions can be as challenging as you want. You never need get bored, you can always challenge yourself to a greater question. And it came along with the beautiful opportunity to teach. Those are the things that drove me back to science, so I’ve had multiple multiple drives all along.

Margonelli: That’s a very unusual path, especially to where you’re working now. I want to know, when did you realize that science was cutthroat?

Elkins-Tanton: You know, I feel like that is an education that most of us get—as I got—during our PhDs. Some people are clever enough to cotton on to this a little sooner. But for the rest of us, there’s really a bit of a professional education during our PhDs, where we learn that we need to stand up and fight for our ideas. We shed that sweet, naive notion that if I do a fantastic study that gives us new insight into the world around us, and I publish it, and it’s peer reviewed, then there it is—people will understand it, and they will adopt it, and it will change human thought.

Very quickly, you begin to realize that that’s not enough. You can publish a brilliant piece of work, but unless you go out on the conference circuit, give talks, engage with other people, have what can be heated conversations, and you’re determined, your information doesn’t really spread. It’s little epiphanies like that, that begin to help us understand what the culture really is.

Margonelli: Did you have a particular epiphany about what the culture really was, where you realized, “Oh, this is really, really highly competitive?”

Elkins-Tanton: There wasn’t one specific epiphany, but I was at MIT for my PhD, a place that I love and have huge loyalty for but which is also absolutely a series of warring city-states among the faculty

People really are fighting for their name, for their results, to be known and not to be dismissed and not to be disrespected, but instead to be adopted by the field and seen for what it is. And I got the feeling while I was there, and also a little bit later in my career, that talking about things like the culture of the laboratory wasn’t welcome. This was around 2000, so it’s only 20 years ago. It’s not totally ancient history. I got the feeling that talking about things like, “let’s definitely take turns speaking at team meetings” and “maybe when you criticize someone else’s work, you could go about it in a more supportive way”—those were thought to be for people who were too weak to make it in the real way. And that if you were really meant to be a serious, top-notch research scientist, you didn’t need to worry about those kinds of things because you’re ready to play hardball. And it took me, oh, about 15 years to figure out what the rebuttal to that was. It took a long time.

Margonelli: I want to move to your rebuttal in a second. I think it’s so interesting because many of us have a really heroic ideal of scientists from movies, from the books that we read, just from our culture. We see them as explorers, visionaries, people who solve problems, moral exemplars, the whole bit. And we don’t really like to think of them as competitive, cutthroat, potentially underhanded, undermining, loud, maybe mean. But let’s talk about this thing that started happening after you’d been in the field for 15 years, and you start to look closer at what was going on around you. You saw something wrong, and you called it the “hero model.” What did you see?

Elkins-Tanton: To address exactly these words that you’re using, I think a lot of scientists are adventurers and explorers and visionaries. And I think a lot of scientists are truly driving forward human knowledge. That’s what science is about‑it’s a way to apprehend the world around us and deepen human knowledge in a way that we hope eliminates or reduces our implicit and explicit bias about what we are observing. We’re just trying to be better observers.

But if you think for a moment, science is a human endeavor. Everything humans do is a human endeavor, made up of humans with all of our faults and foibles and all of our inclinations. And of course, there are people in science who want to be famous. And of course, there are people in science who want to be lauded as excellent and people who want to win awards. I think it’s true in every field of human endeavor. And in science, unfortunately, it does pull us a little bit away from the reason that we’re there in the first place.

While I was a management consultant, I had this sort of epiphany moment around what it can be to work together—where it’s not always each person wanting their own reputation to be the more famous, it’s not always each person trying to be so careful not to ask a question that might be viewed as stupid, or to show weakness. Instead, you can have a circumstance where everyone is working together to create an outcome which is more important than their personal fame. This was a moment working with what was then Touche Ross Management Consulting in Philadelphia, and it’s now at Deloitte. We were working with a client around an issue that the client had, a big client. And we started as a team, envisioning how we could organize ourselves and the client in such a way that we would have a better outcome. And we made up this construct in our heads, and then we convinced everyone to do it. It sounds so simple, right? We all sat around, thought of a way to change, and discussed it. And then it happened. It was an organizational change. It was how the team was going to be organized, the actions that they were going to take, and the outcomes that they were going to make.

That was in stark difference to the kind of science I was doing, where you can’t just imagine what the outcome is and then make it happen. You don’t make it up in your head and then it becomes real. Suddenly, I realized that in the human endeavor, that is what we do. We agree upon how we’re going to organize ourselves, we agree upon the culture we’re going to take, and we agree upon the outcomes we’re trying to create, and magic—it happens. And that was the reason why I realized that in science, we could be doing these great outcomes, we could be creating this new knowledge, but in a construct that was more human and inclusive and positive and effective. We could make up that part of it.

Margonelli: If I can just back up, I think that what happened here is that a management consultant went to some of the fanciest labs in the country and said, “Why are they managed this way? Why are people interacting this way?” I think that’s what you’re saying. And I need you to give me a picture of how science labs are organized and why you called it “hero science.”

Elkins-Tanton: Yeah. Let’s go back to what was happening in 19th-century Germany that was then carried forward to other parts of Europe and to the United States. And I’m going to give you what’s a little bit of a caricature, but for anyone who’s active in research science, I think you’ll also absolutely recognize it. It’s a circumstance where one professor is the person who personifies their subdiscipline at that university. They own that field, they own that body of knowledge, they are the expert in it. And they also own a pyramid of resources. In extreme cases, that includes junior faculty hires along with lab techs and staff to run their organization, and graduate students, and sometimes undergraduate interns, postdocs, and then budgets and equipment, and access to that equipment. So there’s a big pyramid of resources, and on the top is the “hero” professor. So, you know, what could go wrong?

Margonelli: So they started this way back in Germany, in the 15 or 1600s—this was the beginning of the German research?

Elkins-Tanton: Yeah. And then it really got developed in the 18th and 19th centuries, when there was actually a recognition there that to become a leading faculty, you actually had to have charisma and fame. And that was part of your job: to stand up there and assert, “I am the expert. Listen to me, I’ll use my deep convincing voice. And I will never end my sentences with an upturned question inflection.”

There was this culture to create the hero. It was a purposeful culture; we wanted our senior faculty to stand up and be heroes.

Margonelli: And now what we’ve done is we’ve imported that over here, many, many years later, doing a completely different kind of science. We’re not looking at ants and saying where are their ovaries? We’re doing a completely different kind of science that pervades our entire lives. And we still have funding, fame, students’ education, discovery, equipment tied up to an individual.

Elkins-Tanton: That’s right.

Margonelli: And so that has become, in effect, a management culture of science. So how did that model go adrift?

Elkins-Tanton: It served us well for almost a millennium, didn’t it? You know, alas, we’re no longer Lord Kelvin, we can’t any longer discover fundamental chemistry in our kitchen. And it’s very hard to make gigantic breakthroughs in individual subdisciplines unconnected to other subdisciplines.

There are many different ways that it can and has gone wrong and ways that it’s still working really well, too. There are subdisciplines that are super fruitful in this model. But one problem is there is a limit to the resources that are available, so people become very protective of their pyramid of resources. In some cases, this even means that they don’t like their graduate students to spend time with other faculty or research in other labs because they want all of their time and attention in that one discipline on their thesis.

So this kind of “team” culture that’s led entirely by one senior person—who I might add, in general, has never had any leadership or management training, or HR training of any kind; they come at us purely as an individual scientist—it can be rife for bullying and harassment. And often, there’s very little transparency to outsiders or other people in the organization, and few paths for help. This is something we’ve heard so much about since the Me Too movement began, there’s been a big National Academy report—we know that there are problems with harassment and bullying in science and engineering and STEM fields.

Part of it is this: there is not a network of resources available for the people in the pyramid, and their entire careers are dependent upon that senior person. So those are some of the ways [the hero model] goes wrong.

And I would just add that another really critical way it can go wrong is that the senior scientist, the hero scientist, is very motivated to protect their intellectual property and not have other people, at their own institution or others, who claim to have exactly the same or better expertise in that area. So new discoveries tend to be in incremental slivers of real estate around that pyramid of resources and knowledge, up until they bump against another subdiscipline. Right away that paradigm is something that has to be broken. We have to be welcome and rewarded for connecting outside of our pyramid.

Margonelli: It’s interesting. So you’re saying there’s two issues here. One is there’s a set of incentives that drive people towards competitive behaviors. There might be bullying, there might be harassment. I think Science in 2017 published an article by two academics called “Bullying Is Real,” which is kind of a wild stage on which to have that realization. And then there’s also this problem with reproducing the science. Nature interviewed 1500 scientists in 2016 and found that 70% of them said that they couldn’t reproduce their colleagues’ studies, which means that there’s incentives in place to publish that do not also incentivize that being good, reproducible research. So there’s a set of incentives for negative behaviors. And then there’s another set of incentives that are hampering progress, or the same set of incentives are hampering progress on big questions.

Elkins-Tanton: That’s right. That’s exactly right. So we would like the questions to be bigger, and we’d like progress toward them to be faster. And we would also like the process to be more rewarding and inclusive for everyone who wants to participate. Here’s really the bottom line. To me, the absolute bottom line is that science is the best way that humans have ever invented to create lasting knowledge, knowledge that we don’t immediately find out is wrong, knowledge that we can actually make progress based upon. It’s the knowledge that gave us the Pfizer and the Moderna vaccinations. These are things that really matter, and this is a process that really matters.

But of course, it’s imperfect. It’s imperfect because it’s done by humans. It’s not that science is either this perfect thing or we stop believing in it. It’s that science as a human endeavor, and like every human endeavor, we can improve it.

So here are some ways that we could make it better: We can remove some of the things that make harassment and bullying possible. We can create new connections. We can reward scientists and other researchers for working across disciplines. And then, how do we stretch out of the subdiscipline model? That’s the second part of it. How do we ask bigger questions?

Margonelli: One of the things that came up in reading your story and talking to you is that while we’re all kind of hung up on the hero model because it seems totally normal to us and it’s a big part of our popular culture; in fact, there are places like NASA that don’t use it. They have a different organizational model. Can you explain to me what these other models could be, and the models that you’re thinking about?

Elkins-Tanton: Yeah, let’s consider a kind of axis of models where on the one hand, we’ve got this hero model of the person sitting on top of their mountain and asserting that what they know is true. And so the product here is knowledge, but it is produced by a person—in fact, a personality, I would say—and that’s what leads to the hero aspect. On the other end might be something where you’re just focused on the product, where you really are looking at an outcome, and the people are a way to create that outcome. A corporate setting is often a situation where that happens, and any place where there’s a project that’s bigger than the individual.

That’s what happens a lot of times with NASA missions. I’m working on one right now, and working on this mission really did lead to a lot of epiphanies for me about how things can work. This is not to say that NASA is without heroes. In a lot of ways, NASA, and all space exploration, is all about heroes. But it doesn’t have to be. Everything we do can be more inclusive, more voices heard, focused on the outcome. It doesn’t have to be about making individual people more famous.

Margonelli: So there’s a couple of things that you do. You’re doing the Psyche mission, I think that has 800 people involved in it. So obviously your management training is a really big deal there, being able to think in terms of what do you do with 800 people. But the other thing is that you’re working with ASU’s Interplanetary Initiative. And you’re thinking about how to create learning environments at the same time, because one of the issues is that the heroes are supposed to train students. And they do train students, but there are a lot of other incentives involved in here which may not end up with students who are set to go to work. So let’s stop and talk just a little bit about heroes and students, and then talk about your approach.

Elkins-Tanton: Yeah, I’d love to, thanks. Of course, faculty at universities and colleges teach classes to undergraduates. So that’s one very important part of our purpose. And our addition to society is teaching people not just content, but how to learn. Teaching people to be learners, teaching people to have agency, teaching people to go out and be effective in the world and in their lives.

That takes on its most focused version when faculty are working with graduate students, students who are getting their masters or their PhDs. They’re really entered into that pyramid of resources because usually they’re doing original research that is based upon an idea that the faculty member had. It’s the faculty member’s idea–usually, not always—and the student’s job is to carry it out and simultaneously to learn. It’s an apprenticeship model. Now, apprenticeship [models], when they’re done well—totally brilliant. The students learn to be a top expert in their aspect of this subdiscipline, and they’re supported by their faculty member, who then writes them supportive letters, and helps them get jobs, and talks to their colleagues about how great they are, and sets them up for talks, and does all the things that that a dedicated mentor can do to help launch their career.

Now, I don’t need to say, that is a lot of work. It takes some emotional intelligence as well as an intellectual and emotional commitment to the student. So you can immediately see, if you haven’t experienced it yourself, how this can either be a beautiful, effective thing or a tremendous tragedy for the student.

So we’re working at the Interplanetary Initiative at ASU not just on different ways to put together teams for more rapid and effective outcomes, and also more positive ones for the team members, but also on the education side. I’ve been focusing a lot on undergraduates because here’s the divide that I’ve been seeing in education: undergraduates, in its sort of pure end number state, listen to lectures and read textbooks and give back the information on a test, which is incredibly passive. We’ve known for decades that that is not the most effective way to learn. But it’s the way that we faculty thing that undergraduates have to learn in order to get all the content that we need to cram into their heads during these four precious years that we have to influence what they know.

But of course, we’re now in the information age, where all information is everywhere. So how about if we teach students instead the skills that they would otherwise have to wait [until] graduate school to learn? What if we teach them how to find information, decide upon its biases and its verity, and know what to do with that information, decide what outcomes they’re looking for, and figure out how to do those outcomes? In other words, how to be a master learner—someone who can actually execute with expertise, someone who can decide for themselves whether the answer is right or wrong.

These things are not what’s usually taught in undergraduate [education]. And they lead graduate students to have existential crises because all the ways they’d been judged a good student in their lives—test scores, grades, sitting still and listening—are now no longer useful. In fact, they’re the opposite of what a graduate student needs. The graduate student needs to think for themselves, find their own information, decide for themselves when it’s right, decide how to take action. So we’re trying to teach all those things as undergraduates; we’re trying to give the agency and the voice to everyone in the pyramid, not just to the hero.

Margonelli: Wow. Okay. So now let’s talk a little bit about how the Interplanetary Initiative is trying to move away from the hero model into a different way of doing research.

Elkins-Tanton: I’m excited to talk about this. So people talk a lot about how do we bring together art and science. And what I’ve mainly seen happen, from a scientist’s point of view, is there’s a hero scientist who’s running this research project. And an artist is just seconded onto their team almost like a mascot, who’s going to follow them around, learn about this, and create some art. And I haven’t seen many cases where that drove forward the science or the art. So I felt like that was an unsuccessful way to become interdisciplinary.

Meanwhile, I start working on the Psyche mission at Jet Propulsion Laboratory and with our many other partners across the country and around the world. As you said, at peak, we’ve had 800 people working on this team. And I see meetings where, in the room, we’ve got, say, three engineers and a couple of scientists, a graphic designer, a scheduler, a budgeter, a photographer, and we’re all working together and everyone is speaking. And we’re all creating these plans and these actions.

It really struck me like, this is such a different model for how people actually sit around a table, plan their actions, and then go off and produce a product. And the thing that I realized was that, in this model, the goal that we’re doing is exterior to ourselves. Everyone is there because they themselves, and their specific knowledge, is required to reach that goal. And that’s not the same thing as in a scientific lab where the one person has the idea—so the goal is almost internal to the leader—and the other people are brought along, maybe almost as observers, in some cases.

So at Interplanetary Initiative, we’re trying to use this other model, where we agree upon an external goal. It doesn’t just come from one person who’s the leader and the thought proposer, it comes from the whole group. We decide on an external goal, we assemble the team of disciplines that are required to reach that goal, and then everyone’s there for a reason, everyone’s voice gets heard, everyone’s knowledge is necessary. You immediately start with a much more equal and collaborative culture, working toward a goal that everyone equally values.

Margonelli: The culture I’ve been brought up in, which isn’t even the culture of science, says, “Well, you know, that’s just much too squishy. Expertise has to have some edges to it, and if you let everything in, you’re no longer experts.” Give me a really close-up look of like, how do you come up with the questions? And how do you compose the teams?

Elkins-Tanton: So coming up with the questions, we’ve been experimenting with different processing, so I’ll describe to you the one that we’re using right now that seems to be working pretty well. But I want to start with a little preamble, which might be a question of, how do scientists and engineers decide the questions that they’re pursuing? Did I start, when I was purely an academic scientist, thinking to myself, “What is the most important thing I could possibly solve with my time and effort here on Earth?” Generally, not. Generally, I start with, “What is the next really cool question that could possibly be addressed with the tools that I have in my tool belt?”—which is a different question, which is a different way to come about your research. That’s not true for everyone. There are labs all over the world where people are saying, “The very most important thing I can solve with my knowledge in the world today is blah, blah, blah.” And whatever it is, they’re going for it. It’s a really big, important goal. But a lot of us start with a little bit closer horizon.

And so what we’ve been doing instead is we do something we call the big questions process, where we bring as many people as we can into a room. The first time we did it, it was 40 or 50 people from the university and from the community.

Margonelli: They weren’t all scientists?

Elkins-Tanton: No, right. So I just invited everyone I thought I could convince to come because it was such a kind of flyer experiment that I was running. This was in 2017. And we’ve updated a little bit, but basically, the process was I invited people I thought would come. I had some deans, I had somebody from business school, somebody from public service, I had somebody from science, I had faculty. And then I had graduate students, and even undergraduates, and also some members of the general community outside the university who were just interested in what we were doing. So 40 or 50 people, very wide range of disciplines and very wide range of experiences.

And we started with a really classic brainstorm, meaning no criticism. Meaning everyone’s idea is received with a welcome. That’s very important so as not to cause people to shut up from pressure. And what we were trying to do is discover what the questions were that needed to be answered to create a positive human space future. What are the most important questions for us to answer to create a positive human space future? And people started thinking of ideas. One idea would be, “How do we make sure that when we are settled on another body, when we become interplanetary on the Moon or Mars, that humans have a structure to interact? And we understand what we’re going to be—who’s our governance, how they relate to each other?” Questions like that, all the way to, “What is a faster propulsion system that will get us to Mars?” And also, “How do we educate humans here at home on Earth so they’ll be ready to be interplanetary?”

So many questions across such a wide range of things. So we wrote them all down. And after we were finished writing them down on the board, we voted on them. We talked about them a little bit but didn’t want to get people into their critical mode.

Margonelli: This actually gives a very interesting view into your mindset, which is that you’re really looking at interactions with humans and then thinking about results, rather than looking to, in the crudest terms, separate the sheep from the goats, which has often been a winnowing process in science of separating out the people who don’t get to talk. And so this is much more about using every bit of information to structure some set of results that you might deliver or act upon.

Elkins-Tanton: That’s so right. It’s the fundamental belief that I have that science and engineering is in service of all humanity. It’s not in service of a tiny club of your closest peers who could recognize or contest what you’ve discovered. That is not a sufficient use of our time and resources. It’s really in service of all of humanity. And so let’s involve everyone in thinking about what’s important and feeling like they’re a part of the conversation.

Now, very, very important distinction: this is not getting rid of the idea of an expert. It’s not downplaying in any way the importance of a deeply rigorous education and an absolutely unflagging determination to find something that is true and not just guided by your own biases. You have to have that. You need to have disciplinary expertise of the deepest variety. But the thing that’s different is that we can bring those disciplinary experts together in groups of people who include non-disciplinary experts and find directions that are even more important for all of us.

Margonelli: I want you to actually talk about what happens when people get interdisciplinary. Then you set up these teams, and then the teams work in a really different way. Can you just talk about that a little bit?

Elkins-Tanton: Right. Let me start by saying that we give little bits of seed funding to these projects to kind of get them going. And of course, the traditional way that a seed funding program works is that individual heroes come and say, “Here’s my proposal for this brilliant idea.” And then they get some money to take back and do with as they normally would.

So that’s not what we do. We do these big questions. It’s a group project. And then around each of the highest voted questions, we invite people to volunteer into teams—all happening in the same afternoon. This isn’t a go-home exercise, this is all happening in real time. And then they have a couple of different jobs to do while they’re sitting together in the room for an hour. What are some milestones that we could reach in one year with a modest amount of funding that would get us on the track toward a solution for this very big question? Some of these questions are questions that would take a lifetime or several lifetimes to answer. But you can make a milestone for the year.

So first of all, setting really big outcomes and goals. And then you have to identify the disciplines that are needed for your milestones that you don’t yet have. Who are the empty seats at the table, so to speak? Then we pick a leader—there’s no leader till then. We pick a leader and we send them away, and we give them about two weeks to come back with a budget and a team and the fleshed-out milestones. The budgets aren’t big, you know, $10,000 $20,000 per year—they don’t even pay for a whole student. But if you have a leader, if you’ve picked a leader who can come back in two weeks with those things, then they’re probably effective enough to go for the year.

And then the big difference is we put them under professional project management. So we actually hold them to their milestones and their goals and their budget. And we support them if they need extra help in a different way. That’s not usual in academia, and I expected people to kind of run screaming—but it turned out people loved it. We’ve had very few teams disband. People really respond to having a question that’s bigger than just themselves. And that’s about being on a motivated team and having the supportive structure. It turns out, it really connects to something deeply human among us, and it’s been really successful.

Margonelli: Wow, that’s such an inspiring model. And you make it sound kind of fun.

If you want to read more about how successful Lindy’s ideas have been, read her article over on issues.org. The way we conduct research could be very, very different.

Thank you for joining us for this episode of The Ongoing Transformation. And thank you so much to our guest, Lindy Elkins Tanton, for talking to us about the problems of the hero model of science, how we can change it, and how to train the next generation of science leaders. Visit us at issues.org for more conversations and articles. I’m Lisa Margonelli, editor-in-chief of Issues in Science and Technology. See you next time.

Episode 1: Science Policymakers’ Required Reading

Every Monday afternoon, the Washington, DC, science policy community clicks open an email newsletter from the American Institute of Physics’ science policy news service, FYI, to learn what they’ve missed. We spoke with Mitch Ambrose and Will Thomas about this amazing must-read: how it comes together in real time and what it reveals about the ever-changing world of science policy itself.

Recommended reading

Find FYI’s trackers and subscribe to their newsletters at aip.org/fyi.

Transcript

Josh Trapani: Welcome to The Ongoing Transformation, a podcast from Issues in Science and Technology. Issues is a quarterly journal published by the National Academies of Science, Engineering and Medicine, and Arizona State University. I’m Joshua Trapani, senior editor at Issues in Science and Technology. I’m joined by Mitch Ambrose and Will Thomas from the American Institute of Physics science policy news service called FYI. Their newsletters and tools for tracking science policy budgets and legislation are key assets in the science policy community. On this episode, we’ll talk to Mitch and Will about their view of science policy and get a look under the hood at what goes into creating FYI’s newsletters and resources. Welcome, Mitch and Will, it’s a real pleasure to have you with us.

Thomas: Thanks very much. We’re pleased to be here.

Ambrose: Great to be on.

Trapani: So FYI describes itself as an authoritative news and resource center for federal science policy. And I’d like to start with a big picture question: How do you define science policy?

Ambrose: So there’s a very classical formulation of that, that’s the two sides of the coin of science for policy and policy for science. And I have nothing against that formulation, I think it is helpful to broadly bin the types of issues you come across. But we don’t really think about it in that way in FYI. We approach it in a variety of ways. We were not thinking about “Oh, this is a policy for science story,” and “Oh, this is a science for policy story.” We’re focused on various aspects of the process. You know, there’s the very formal set of budget documents that gets through the annual appropriations process following the President’s budget request to the House and Senate appropriations bills to the final outcome. There’s a whole procedure around that and a whole cast of characters involved in that process, and really getting a sense of what individual people’s priorities are, and the whole machinery of how priorities get set. And that’s just one lane of science policy. But then there’s all sorts of other lanes as well that we pay attention to. So we take this very procedural focus, I would say.

Thomas: Yeah, I think of it as a very empirical approach. What is science policy? Well, it’s what policymakers are talking about. What challenges are they facing? What opportunities do they see? What proposals are they putting out there? And what kinds of arguments are they making for and against different sorts of things? And when you do that, you pick up on a lot of things that maybe you didn’t even think about as science policy ahead of time, or you find that there’s an issue in some area of policy, like trade relations, for example, that turns out to have a very technical dimension to it. The nice thing about taking that approach as a news organization is that you’re always talking about things that are definitely on policymakers’ agendas, whether it’s in Congress, or in the agencies, or in universities, or among advocacy groups.

Trapani: How do you draw that line? If there’s something that maybe hasn’t traditionally been part of science policy but it comes up, how do you decide: this is in or this is out? Or is that not how it works?

Thomas: Yeah, I think it’s a really cogent question that we’re asking ourselves all the time. You know, we work for the American Institute of Physics, which is a federation of 10 member societies—American Physical Society, Optica, the American Astronomical Society, and so on. And so we’re always asking ourselves, “What sorts of issues might they be interested in?” That’s a very practical way of delimiting ourselves because we’re a team of four people and we can only cover so much.

And then there are certain issues that just kind of creep onto our agenda after a while. For example, the meteorologists have been concerned for quite some time about federal allocation of radio spectrum because with new 5G devices coming online, that can interfere with weather satellite observations, for example. And so for a long time, we were interested in this issue in a very, very top-level way. We just saw spectrum meetings, and we took note of the fact—“Oh, there’s something with spectrum going on.” And then starting about two, three years ago, this became a really, really serious issue. We decided that we had to learn about it because there was a lot of action going on, a lot of arguments between federal agencies—between the Federal Communications Commission, NASA, the National Oceanic and Atmospheric Administration, Department of Defense—and so suddenly, something that had not been part of our agenda at all was part of our agenda simply because it cropped up and you couldn’t ignore it anymore.

Ambrose: I’d like to build on Will’s comments there. To the broader point of how would you bound science policy—setting aside bandwidth constraints—how would you bound the topic space? I would say, well, one approach would be: OK. There are various committees in Congress that have control over science. There’s the appropriations committees, and there’s no one appropriations committee for science; it’s distributed across many different subcommittees. So the subcommittee that funds the Department of Energy also funds the Army Corps of Engineers. And then there’s a separate subcommittee that funds NASA, NSF, and NIST, but also the FBI, and all sorts of other agencies that have nothing really to do with science policy, in a narrow sense. So you could take a very structured approach to just looking at specific committees that have jurisdiction over science.

But what we found, especially over the past few years, is that science policy is cropping up across many, many committees that we would never expect. The Judiciary Committee, for instance, is considering immigration reforms, some of which have very big implications, potentially, for the science workforce. You have many, many committees, beyond even the Intelligence Committee, that are getting interested in the topic of what I’ll call research security, which is largely tied to the US-China dynamic, where there’s many people across committees in Congress that believe that China’s taking advantage of the US research system in various ways. That’s just become such a burning topic that it’s showing up in all sorts of places we never looked at.

We have a congressional tracking service. And I had a keyword search for various science terms. All of a sudden, I started hearing the FBI director start talking about science. And I’m like, “Huh, that’s interesting.” So there’s a whole new set of institutions that we had to learn about, as essentially an emerging area of science policy. And I would say as well, and we can get into this later—there’s a whole series of prosecutions of scientists through the Justice Department’s China Initiative. For my first few years in this job, I never looked at court documents at all—it just didn’t come up as an area of physical science policy. But now, when scientists are starting to get prosecuted, now I’m looking through the PACER system, and it’s a whole new set of procedures that I would argue is now part of science policy based on the current dynamics.

Thomas: It’s an interesting thing. I mean, in 2018, when the FBI director first started talking about this, we were one of the very few organizations that was really paying attention. And we noticed that it started cropping up in additional congressional committees, and there were a series of members of Congress who were really interested in the issue. So now you have large petitions at Stanford University and other universities and large protest movements against this China Initiative. I’ve seen it on the evening news—but that’s been only within the past year or two. By taking this empirical approach, we’ve been there all along, and we’ve been tracing the different facets of the issue. That’s one area where our, for the lack of a better term, empirical approach has really kind of paid off.

Trapani: This is really interesting, you have this really broad, comprehensive, holistic view of science policy that lets you almost see out ahead of where things are. I was wondering if you wanted to provide any insights on what you see as the most important things happening right now, that people either aren’t paying attention to or aren’t paying sufficient attention to, in the realm of science policy?

Ambrose: To build on what Will just said, I would say the China Initiative itself is something that wasn’t being paid attention to enough until fairly recently. And now, as Will mentioned, you have these campaigns of scientists at different universities that are starting to really mobilize around that issue. When this initiative was first announced, I think in late 2018, it took quite a while—after a few of these prosecutions of scientists to sink in, you know, what are the effects on the academic community. And now people are going to pay much more attention to it, and it’s getting a lot more media coverage broadly. So I would say that that issue is now getting the attention it warrants.

But there are others, like the spectrum issue that Will mentioned is another one that really burst onto the scene. And you know, the FCC, if you had been following filing documents for FCC going back several years—and this was actually the topic of a recent hearing in the Science Committee where essentially the chair of the committee, Representative Eddie Bernice Johnson, made the point that had the science agencies been paying attention to these FCC proceedings more closely, they would have been able to see this coming years in advance, this issue of spectrum interference with Earth observation satellites or astronomical observations. But it was just a foreign area of policy, even to the science agencies themselves, and it’s quite arcane. And she made this remark about essentially, you need lawyers to decipher this sort of thing for you. But now that issue blew up, you had these fights between agencies over spectrum allocations, and now it’s getting quite a bit of attention.

One other one that I’ll mention quickly is this issue of light pollution from satellite mega-constellations. And what it really took was that first launch of a bunch of satellites from SpaceX’s Starlink constellation, and then the astronomers are like, “Oh, no, this is gonna be a huge deal.” So now it’s getting a ton of attention. There’s a few issues like that, that just within the past few years have burst onto the scene for our reporting, that I think if people had perhaps been a bit savvier, [they] would have seen them coming down the pipe. We didn’t forecast those issues until they burst into public view ourselves, so we’re not claiming special knowledge in this area. But I think those are some good recent examples of how these hot topics can really come out of nowhere, almost, in science policy.

Thomas: One thing you asked, is enough attention being paid to an issue? And the question is really, attention by whom? Sometimes there are people who are fairly niche who are really, really interested in an issue, and nobody else pays any attention to it whatsoever. So FCC filings, for example, the telecommunications industry is paying attention to that all the time. But scientists weren’t. The scientists didn’t know how to do it. Scientists’ lawyers … didn’t know how to pay attention to it. And so it’s only recently, years after the initial filing, that they really glommed on to it and said, “Hey, actually, this is a really important issue, and it could cause us some fairly serious problems.”

Similarly, we have two issues that are really big in science policy right now—we mentioned the China Initiative and all these arrests of people with Chinese backgrounds, be they immigrants from China or visitors from China or simply Chinese Americans, and then you have other sets of people who are interested in diversity, equity, and inclusion issues. And those really aren’t the same groups, even though they’re united by a common cause of justice and civil liberties and that sort of thing. So one of the things that we hope that we do—we don’t know if we do it or not; we don’t know how effective we are in doing it—is if there’s a fairly niche issue, or if there’s a community that should be paying attention to it, that we can help alert them to the existence of these issues and help to get them up to speed on the nitty gritty of it as best as we can.

Trapani: I’d like to turn to FYI itself. There is a lot of reporting, there’s a weekly newsletter, there’s a budget tracker, you track people in the science policy world. And it gets circulated around the science policy world quite broadly. Before I came to Issues in Science and Technology, I was in several other science policy roles, and when I first learned about FYI, I was like, “Oh my gosh, I have to go subscribe to this immediately.” I learned about it in a way that a lot of people do, which is people will forward it on or forward on chunks of it.

The thing is that once you subscribe, you realize that a lot of the really smart people who seem like they’re in the know in your organization are actually just forwarding on bits and pieces of FYI. And then you get to laugh at those people in your mind. But within a few weeks, you find yourself turning around and engaging in exactly the same behavior, because it is just such a valuable resource for the community. I was wondering if you could just talk a little bit, because it is so comprehensive, about how do you go about gathering up all the things that go into the weekly newsletter or the other tools that you have? And what kind of analysis goes into that?

Thomas: That is really our secret sauce. I’ll let Mitch take the lead.

Ambrose: I’d first like to just sketch a bit of the history of FYI, I think it’d be instructive at this stage. It was started in the late 80s by essentially one person, Dick Jones. And at that time, it was just distributed literally by paper mail for the first few years of its existence, but it did have certain elements that have continued through today.

As I mentioned at the outset, we have this very formalized way of covering the federal budget process, for instance. There’s a series of documents that are produced through that: there’s the President’s budget request, then the House Appropriations Committee advances its set of bills that have reports with all sorts of detailed policy instruction. Then the Senate will eventually do its version of those same set of reports. And then they finally, usually several weeks late, have a final agreement. And there’s documents associated with every stage and from its outset, FYI, its bread and butter has been stepping through those foundational science policy documents. And that continues through the current day, except we’re much more in-depth than we used to be, which I’ll get into in a bit. And also, FYI covered a lot of speeches from policymakers, and did still have that kind of people-focused approach. But it was essentially just one person, for the most part, up until when the founder, Dick Jones, retired in about 2015.

And then AIP reflected at that point, people seem to really like this type of information. Let’s really scale this up. So over the coming couple of years, we scaled up to four people. And that has really enabled us to [make] a sea change in FYI reporting, where we launched this weekly newsletter, called FYI This Week, that is giving you a preview of what’s coming down the pipe in the coming week or so, a summary of the big things that happened in the previous week, and then all sorts of additional information like an event calendar and a roundup of job opportunities, and also a roundup of other people’s reporting. And we’re very generous in acknowledging just good science policy reporting that we see. Every edition has about 100 or so links at the end. It’s almost like this little appendix of interesting science policy articles that the team sees throughout the previous week.

I’m always floored at how much science policy reporting there is, if you just know where to look. And it was in the process of constructing this very comprehensive, weekly newsletter that we started to really formalize a way for surveilling what’s going on. And we have all sorts of fishing lines, I like to think, out looking for relevant events, relevant reports, there’s a series of information streams that we’ve set up in order to have this week-over-week reporting on what’s happening—and trying not just to catch the newsiest things. We do give those more attention, but also including all sorts of links to less newsy things—but you can kind of see something’s bubbling up. And so we have this way of, across the whole landscape, we try to pay attention, essentially, to everything—as much as we can at once. I can’t say everything at once. By paying attention to the entire landscape, or as much as you can at one time, then you can start to see these little deltas of activity in different committees or different agencies. And then eventually, that might bubble up into something that we write a full article about. And that’s, we have this thing called the FYI Bulletin, which has existed from the beginning, which is our full length reporting. So we have this interplay between the weekly newsletter, which is OK, here’s the week-to-week churn, and then once something becomes a big enough story, we do a Bulletin on it. And I’ll stop there and see if, Will, you want to add to that.

Thomas: Yeah, I mean, it’s really just being knowledgeable about what sorts of documents are apt to contain… We develop a baseline knowledge of what exists right now, we call it the landscape of science policy. And the more you can know about that, the more you can see where it changed. Mitch is apt to call this a delta, with his physics background—and then you learn about the windows where these things are apt to come out.

So I mentioned the documents, but there are also congressional hearings. And you know, 95% of what’s said at a congressional hearing is not, frankly, going to be very interesting—to be honest, like 99%. But there’s always going to be some little thing, maybe it’s in the opening statement, maybe it’s later on in the hearing, maybe it’s something the witnesses say, and you can glom onto that, if you know what’s already out there, and say, “That’s new, I have not heard that before. This is something that we have to pay attention to.”

All the federal agencies have these advisory committees of outside scientists, and that tends to be where they talk about what’s going on with their programs. Is something over budget, is there something that they’re worried about, what’s their latest initiative in research or in some other aspect of their activities. It used to be that we would be listening to these things live, and that ultimately became untenable because one, there’s only going to be a little bit that you really, truly need to pay attention to, and also there’s lots of different things going on at once.

So we started to be a little bit smarter about recording these things, feeding them into these new AI transcription services so that we can scan what was said a lot more easily, and it’s been just really a series of small innovations that lets us consume more and more and more, even though we’re a really small team, we can pay attention to an astonishingly large amount of things. And then things that we miss, we depend on reporters for other outlets. We can say, Science magazine, it has excellent reporters, SpaceNews is just awesome, awesome reporting in the space sector, Nature—it goes on and on. What does it, Mitch, National Journal that’s been reporting on the science policy legislation?

Ambrose: Yeah, there’s a particular reporter at National Journal who’s gotten very interested in science policy.

Thomas: And they just came out of nowhere, and they do a lot of important work for you. And we always say like, we’re only four people, we’re not going to only cite ourselves, because there are a lot of people who are paying attention to a lot of things we simply can’t pay attention to. And we want to acknowledge them as part of this science policy news ecosystem.

Trapani: It’s remarkable how much information you all process and put into your stuff. I would have thought that there would have been an army over there, so I was really curious as to how you did it. It sounds like FYI has grown—in terms of sophistication, in terms of people—in terms of the issues over the last few years. What do you see as coming next for FYI? Or what would you like to see next?

Ambrose: One other thing I didn’t mention that we launched over the past few years, in addition to the weekly newsletter, is we have this series of trackers. They’re essentially landing pages on our website. We have a budget tracker, which has very fine-grained information on, for a given agency, what is the funding outlook for [a] particular project? Then we have a leadership tracker, which is the “who are in positions of power over the science agencies in some way?”—both people going through the Senate confirmation process, but also a whole constellation of people who are career officials that don’t typically turn over with a given administration. And then finally, we have a bill tracker, which is an index of key legislation relevant to the physical sciences.

It just gives you this whole map of in these different categories of data—budget data, people data, and legislative data—what’s going on? To your question about some new things we’d like to do right now, each of those trackers is in a pretty rudimentary stage. We’d really like to take each of them to the next level, and build out what we kind of call to ourselves an information architecture. And how do you provide some extra context around all the information that’s provided in there? Particularly with budget information for large facilities as they’re going through the process, for instance, it can be difficult to interpret the significance of certain changes in the funding profile.

Thomas: If I can offer an example: NASA launches science missions, right? And their funding will go on an arc, and one day, you’ll see they’re going to cut the budget for the science mission by 80%. Well, yes, that’s because it’s launching—it’s not because they don’t like it. And we don’t communicate that in any way in the budget tracker. As it stands right now, you just have to be aware of that, whether by reading our bulletins or because you’re an insider. So that’s what Mitch means by context.

Ambrose: We think you could even make, beyond just the contextual information, you know, building it into a richer resource for people. The astronomy and astrophysics community just came out with their latest decadal survey, an extremely important prioritization report for that discipline. And a big part of that exercise is, you know, constructing different budget wedges of, how much money will we have in a given amount of time to do a flagship space telescope mission versus a ground-based telescope? And how do we fit that under certain budget guidance that we’ve gotten from the agencies? We feel like we could, for instance, build out our budget tracker into a tool to help those types of planning exercises—really look at what past budget wedges were like for different sets of projects, how did that fit under a given constraint. And also looking forward as to what the current projections are, adding that up in what’s known as a sand chart and seeing if you’re going to be able to fit under a certain budget target. That’s just one of many examples I could give of how you could make richer information resources that aren’t strictly news, per se, but we think could provide, both in aiding our own understanding of these processes but also providing tools for the scientific community to understand what’s going on. So a lot of this falls under a concept we’ve thought about of establishing almost like a research hub for science policy to complement the journalism that FYI does.

Thomas: When you’re a news organization, you accumulate a lot of information over time. Something you knew as a fact last year may no longer be true this year. And you can follow FYI, maybe if you do very studiously, you’ll be really up on the issue. But if you haven’t been an absolute scholar on this issue, we’d like to have a place where you can go so that you can learn everything that we’ve learned about this issue and have the most up-to-date information. And that would really make us almost as much of a research organization as it would make us a news organization.

The fact is, we have four people who work for FYI: me and Mitch, Adria Schwarber and Andrea Peterson, and none of us have backgrounds in journalism. We all are in science or history of science or something like that. We’re all researchers in one way or another. And so we’re not, in some sense, content just to write news articles—we want to share our knowledge with the world, so to speak. That’s kind of the central idea, is becoming more of a research organization. There are multiple ways in which we can do that. And expanding our trackers, creating these issue guides, those are two facets of what we’d like to do. We just have to find a logical way to expand that doesn’t put too much pressure on us because we’re pretty much at the red line as it is.

Trapani: Figuring out how to expand, reach new audiences, and create new resources while at the red line is a challenge for Issues too. We’re inspired by what you’re doing at FYI. On behalf of myself, Issues in Science and Technology, and probably thousands of people who work in science policy fields, I’d like to thank you for all you do and all the tools that you put out there.

Thomas: Thanks so much! It’s been really enjoyable.

Ambrose: And thanks for the opportunity to come on the podcast.

Trapani: Thank you for joining us for this episode of The Ongoing Transformation. If you have any comments, please email us at [email protected] and visit us at issues.org for more conversations and articles. I’m Josh Trapani, senior editor of Issues in Science and Technology. See you next time.

A Veneer of Objectivity

Accounting for Lives Lost

By some estimates at least 1.8 million Africans lost their lives during the transatlantic slave trade. Using an online database called Slave Voyages, artist Kathie Foley-Meyer studied maps detailing the paths that slave ships took from Africa to the Americas. Foley-Meyer, a PhD student in visual studies at the University of California, Irvine, created the painting In the Wake: With the Bones of Our Ancestors in an effort to remember those who perished before making landfall. “I just became obsessed with the lives of these human beings that are accounted for, but not really,” she told a university reporter. “They exist as bodies that disintegrated when they were put into the ocean and became part of the oceanic life cycle.” 

She continued, “I remember looking at the statistics of human cargo—the number of people that survived the voyage to the New World and the number of people who did not. I began to wonder, other than numbers, how do you account for those lives lost? Those people were taken from their homeland and deposited in the ocean for one reason or another that rendered them disposable and not recognized as human beings.” 

Foley-Meyer is a participant in the Ocean Memory Project, a collaboration of scientists, artists, engineers, and designers who are exploring the question, “Does the ocean have a memory?” The project is funded by the National Academies Keck Futures Initiative and is led by National Academy of Sciences member Jody Deming. 

Image courtesy of the artist.

Ethics and Policymaking

S&T Policy and Economic Security

Solar Climate Intervention

A New Model for Research Teams

Climate Scenarios and Reality

Note: Chris Field and Marcia McNutt’s letter has been updated to include a more complete quotation from the original essay by Roger Pielke Jr. and Justin Ritchie.

“Science and Technology Now Sit in the Center of Every Policy and Social Issue”

In January 2021, President Biden appointed sociologist Alondra Nelson, a leading scholar of science, technology, medicine, and social inequality, to be the first deputy director for science and society in the White House Office of Science and Technology Policy (OSTP). Issues in Science and Technology editor William Kearney recently spoke with her about her role in bringing social science expertise to federal science and technology (S&T) policy and the Biden administration’s goal to make that policy fair and equitable for all members of society.

You were writing a book about OSTP before your appointment there, and you’ve followed the ways its role in federal science policy has fluctuated over the decades. President Biden immediately heightened its role, however, when he elevated his science advisor, the OSTP director, to his cabinet. What is the significance of that move?

Nelson: I started doing the research for the book because I found it such a fascinating office for somebody who is a student of science policy. In the 1970s, the OSTP was originally imagined to be a small shop, but what’s happened over the intervening decades is that science and technology now sit in the center of every policy and social issue. And so it only makes sense—when I track evolution of this work with my academic’s hat on—that at this moment it would be a cabinet-level office.  

In answering your question, it is also important to think about the current context. Every president faces profound challenges and a unique set of historical circumstances when they come into office. For President Biden, this was a once-in-a-century pandemic combined with a climate emergency—all in the context of a growing awareness of injustice and inequity in American society, and globally. Every dimension of national and international policy, from health and education, to security, to social welfare, and everything in between, has something to do with science and technology. There’s no way to tackle the major challenges and opportunities we face without engaging science and technology. From that perspective, and given the president’s commitment to having a government that is evidence-based and informed by science, it follows that this would be a cabinet-level position. I think that the fulfillment of the aspirations and values of the Biden-Harris administration are manifest in the elevation of OSTP’s directorship to the cabinet.

OSTP is still a small shop compared to big agencies, so how do you coordinate science policy across the entire federal government so that it aligns with President Biden’s goals and vision? Is that the job of OSTP?

Nelson: Strategy and coordination are part of OSTP’s founding mission. We work in parallel with, and administer, the National Science and Technology Council (NSTC)—about which I think not enough is known by the public—to coordinate interagency alignment with the administration’s priorities. NSTC was established in 1993 and there is now a nearly 30-year infrastructure for doing exactly the kind of interagency work you suggest. NSTC is doing work on critical minerals, advanced manufacturing, scientific integrity, STEM equity, algorithmic accountability, and many of the other big issues we face. There are interagency folks at the table, sitting with OSTP colleagues, working to create strategy and policy.

On the eve of his inauguration, President-elect Biden wrote a public letter to Eric Lander, who he had nominated as OSTP director, tasking him with answering five big strategic science and technology policy questions. Among them was, “How can we guarantee that the fruits of science and technology are fully shared across all of America and among all Americans?” How are you trying to answer that question? What would success look like?

Nelson: The question President Biden posed to Director Lander in that letter suggests what is distinctive about this OSTP—and what I find really exciting about it. The question is the foundation of the Science and Society Division, which is a new division that I have the privilege of leading. Every day we are working with public servants, researchers and scientists, policymakers across government, and sectors of the American public to answer this question.

The goal is to build a science policy that intentionally and explicitly includes the perspectives of the American public, including seeing science and technology through the eyes of folks who are marginalized or vulnerable. This approach to policy views innovation as something that has been extraordinary and offered great progress and promise to some people, but has also sometimes come at the cost of harm and damage to other communities. And in this moment in which there is diminished trust in institutions and diminished trust in science, it means bringing S&T policy development out of the shadows. A phrase I often use is “showing our work.” For the government, that means being more transparent about the past, about what we’re doing in the present, and about our goals for the future. What you’ve been hearing in the language of the administration is an explicit effort to situate science and technology policy with democratic values, including inclusion, accountability, justice, and integrity. The challenge is to drive, design, and implement policy with those values always in mind.

What would success look like? A STEM workforce that really looks like all of us, that reflects all of us, in the classroom and in the boardroom. Empowering new communities to be at the table of S&T policy. I think success looks like a public that feels that it can be engaged in the work of government; a lot of work we are doing in OSTP is conducting listening sessions and using other ways of engaging the public to help us think about the work we do. Success also includes a new set of rules of the road, such as an approach to innovation that is rooted in inclusion and scientific integrity. It means having a sense of responsibility to have aspirations, safeguards, and values in place that can help ensure that folks are not abused or discriminated against as new S&T comes online—to ensure, per President Biden’s question, that it really benefits all people.

You said there’s a need to be transparent about the past. What do you mean by that?

Nelson: The Biden-Harris administration has set out to pursue racial and economic justice in every facet of our work and to address head-on disparities and inequities that exist because of things that have happened in the past and continue to happen in the present. Disparities in medicine, health, and access to education didn’t just appear overnight; they congealed over time, one generation after the next, one injustice on top of another. Even those of us who might consider ourselves technophiles and science optimists grew up hearing stories of tragedies, and indeed horrors, in the past. The story that we hear most about is the Tuskegee syphilis experiments, which I often remind people was a project of the US Public Health Service, not just something that just sort of emerged or was in the private sector. That was 40 years of government research. 

We need to say that we know science and technology has not equally benefited all people. We stipulate that at the beginning. As I said before, in a context of low trust in government and institutions, it’s incumbent upon government, in a very profound way, to be forthright. If we are really going to be in service to the American public, we need to have some difficult conversations. I think from honest accounting we can move into truly innovative and mutually beneficial S&T policy and outcomes. 

A couple of examples are the listening sessions, which I mentioned earlier, hosted by the Scientific Integrity Task Force. The task force was established through a memorandum from President Biden and was asked to recommend policies and practices that can prevent political interference in federal science, with the aim of restoring trust in government. Part of the work of the task force has been an accounting of lapses in scientific integrity as a necessary part of the process of suggesting a way forward. A second example is the Equitable Data Working Group that I cochair. This was established on the first day of the administration through an executive order on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government. This group is attempting to identify and fill in demographic data gaps to help answer the question of whether or not government is doing its work equitably. We need to be honest that in many instances we couldn’t answer that question in the past because we didn’t have the data we need to do so.

Almost 20 years ago you coedited a book, Technicolor, that challenged some common assumptions about the relationship between race and technology. What misconceptions persist about the so-called digital divide?

Nelson: I’ve been thinking about these issues for a long time. Technicolor was framed around early conceptions of the digital divide. A stereotype had emerged, a kind of false narrative about technological evolution, that held that progress had been forged largely by white scientists and technologists, white innovators, and white inventors, and that the other side of the coin was that people of color were somehow less capable when it came to technology. I think now we are a little more aware as a society that that framing is incorrect; there is a rich history of Black and brown scientists, inventors, and innovators who’ve achieved critical breakthroughs, often against incredible odds. In that early work, we were trying to surface some of that history and explore the idea that the digital divide, at its worst, can become this kind of self-fulfilling prophecy, a kind of fiction that people of color can’t keep pace in a high-tech world. We shouldn’t accept the notion that working-class people, or people who haven’t had certain kinds of educational benefits, are less competent, less interested, less passionate about, and less innovative in science and technology. We’ve got to think in different ways about the digital divide.

In this moment what’s true and important about the digital divide is the extent to which it offers us a prism for understanding infrastructure inequality in the United States. Certainly, COVID-19 shined a light on a range of disparities, including the inability of many to get online to work remotely or to give kids access to schooling. I’ve been proud of what the administration has done to measure those disparities and to also try to address them. The National Telecommunications and Information Administration, which advises the president on telecom issues, published this incredible mapping tool where you can actually see the places and populations with more reliable or less reliable broadband coverage. The Biden-Harris administration is planning to invest $65 billion to connect Americans to highspeed internet.

How do we change the thinking about where innovation comes from?

Nelson: We know from the organizational behavior literature that it is diversity broadly—not just racial and ethnic diversity, but broad diversity of perspective and experience—that is one of the most significant drivers of innovation. When we are setting the conditions for innovation in science and technology policy, it is a shame if we are not also leveraging this one demonstrated driver of innovation. We need to get more people involved in the work of doing science and technology policy and, of course, science and technology research and development itself. The United States is this great lab of innovation, and we should be able to turn that innovation into products and practices that not only take on hard problems like climate change and pandemics but are also more equitable.

Do you see social science becoming a bigger part of the policymaking toolkit?

Nelson: I certainly hope so. This in part is why I am at OSTP. To go back to our earlier conversation, many of the tools that we need for robust government—tools for understanding the lived experiences of the American public; for assessing the equitable, successful delivery of government services, for identifying demographic trends in economy, labor, and STEM professions; for applied data science across pressing policy areas—come from social science. How do we assess whether or not programs are serving intended communities? Is this federal program serving hard-hit communities in low-lying lands that are more likely to be exposed to climate change? That, and many others, are empirical questions that can be answered when we apply social science concepts to qualitative and quantitative data. The answers we generate can then inform policy. 

I think that as government becomes more analytical, it is very important to have social scientists at the table. One of the most important reasons is because we think about answering questions with different kinds of data, produced using both quantitative and qualitative methods. And as much as the technical analysis matters, policymaking is always going to involve that social piece, that human piece, that historical piece. I hope a new way of thinking about not just S&T policymaking but policymaking more generally can be found in social science, which helps us see tensions in society, map them, reconcile them, and understand them, and recommend changes more conducive to equitable experiences and outcomes among all members of society. I believe as a scholar and researcher, and as a policymaker, that social science evidence, at its best, really can point us to better policy solutions.

How do you communicate to the public the urgency of climate change or other pressing issues in the midst of a still overwhelming pandemic?

Nelson: One of the lessons of COVID-19 is that, in some way, we all became social scientists. It is this moment, I think, in which all of us had to come to terms with the profound complexity of the challenges that we face right now, and in the coming years. There were times in the pandemic when all of us became armchair epidemiologists, making risk assessment calculations for our families, for our neighborhoods, for our workplaces and schools. 

At the same time, the science and technology around the pandemic was extraordinary: we decoded the genome of the virus in a month or so, we had a vaccine in less than a year. Yet we realize we have not conquered it. It has not been for lack of science and technology that we have not conquered it, but because of the environment in which that science and technology emerged—these are profound social questions. And when it comes to climate change, we’re living in a time where the impact is acute, it’s urgent and existential. I want to believe that all of us in the American public are learning to face up to the complexities of climate change, and the pandemic may have primed how we think about it. I hope that presents some opportunities for courageous possibilities for both domestic and international climate change policy and for pandemic preparedness.

Is there anything else you would like Issues’ readers to know about President Biden’s science policy priorities?

Nelson: I would like your readers to know that the federal R&D budget for the 2023 fiscal year not only puts a priority on cutting-edge science and technology, but it also puts a priority on innovation for equity. We’re proposing a new kind of social compact for S&T policy, in which it is pursued in the context of the social ecosystem it sits in, with a greater awareness of whom it’s supposed to benefit—and how.

A Revolution for Engineering Education

Cognitive Ecosystems

Beyond Trust in Science

Principles for US Industrial Policy

Democratizing Talent and Ideas