Prompt: Indeed, many drivers are confident they can reach higher-ups in the platform through their community structures. “When we pick up the phone, or tweet to the company and tell them we’re part of a so-and-so community, they have to sit up and listen,” one driver told us. “We are not anonymous nobodies—they can’t ignore us.” Power Brokers Life in the Global South has always been precarious since the advent of global capitalism, and many workers have turned to each other to mitigate their vulnerability. The shape their relationships take is, of course, informed by individual context and constraints. In Bengaluru, drivers have developed forms of support and worker power outside traditional unions. But, unlike in Jakarta, they often look to existing power brokers, especially political parties, to help them challenge their working conditions.  In Bengaluru, a substantial portion of app-based workers are migrants from other parts of Karnataka state. Historically, they have been vulnerable to xenophobic attacks from nativist groups seeking to keep the city for locals only. Insecure in their position in the city, Bengaluru’s platform workers rely on ties with relatives, friends of friends, and fellow migrants from their home districts. They look out for each other as the gig workers of Jakarta do: answering distress calls in the case of road accidents, helping each other navigate interactions with the police, and standing up for drivers who are reported to platforms for misconduct. But, unlike in Jakarta, where identity as a gig worker is the sole basis for collective organizing, in Bengaluru workers are bound together by numerous forms of kinship that are based on different combinations of shared caste, religion, or place of origin. Aid and support are furnished within these kinship groups.  App and other workers in the city have built solidarity over the years—but the path to that solidarity was shaped by varying degrees of suspicion and antipathy towards migrants, non-male people, and non-local language speakers. It was also shaped by platform workers’ status as new entrants into the market. In 2016, in the early days of Uber and its Indian counterpart Ola’s operations in the city, drivers faced a lot of hostility from auto rickshaw and traditional cab fleet drivers, because the app-based drivers were technologically equipped to provide on-demand services and not fully subject to existing transport laws, allowing them significantly more freedom in terms of area of operation and permits required.

Response: Platform drivers were also not given the choice to join existing transport workers’ unions. As in other parts of the country, this was partly because local and regional unions are embroiled in urban, linguistic, religious, and other alliances to sustain their power. In addition, traditional unions were often antagonistic to workers disrupting the existing labor market. As a result, when early conversations around forming an association of app-based drivers began, the drivers who wanted representation sought affiliation and support from the political party in power in the city, signaling that new transport workers’ loyalty to the party could be valuable for future elections.


Prompt: This strategy was informed by the app workers’ awareness that to broker agreements with tech companies (such as getting an increase in the minimum earnings on a ride or delivery whenever there was a rise in petrol prices), engage in visible collective action (such as strikes), and make demands to improve their working conditions, they first had to build relationships with local politicians, government officials, and city elites. App-based driving eventually became the normative, though not dominant, mode of work. Traditional and local workers realized that opposing platform work would harm their own communities, including rickshaw drivers and unemployed Kannadiga youth.

Response: Since 2016, a number of self-proclaimed leaders and representatives of gig workers have forged many such alliances with city elites, local and national political parties, and social organizations—from nativist, Hindu-majoritarian organizations to broad work-based alliances—to continue to gain power. Strikes or protests against their working conditions are only undertaken in partnership with these parties and power brokers. Indeed, even as worker unions and driver associations have cropped up in every major Indian city in recent years, they remain deeply involved in ongoing struggles over who belongs to the city and who can exercise power within it.


Prompt: Single Fighters Not all platform workers in these cities organize, either formally or informally. In Jakarta, many drivers choose not to be part of communities, becoming instead what are locally referred to as “single fighters.” Membership in communities comes with high expectations, such as being present in the basecamp and being active on the WhatsApp groups. Drivers have reported feeling pressured to miss orders to hang out with the community, or generally being unable to afford volunteering their time for mutual aid activities. While such expectations are not formally enforced, there is an informal code of reciprocity; if drivers become “inactive,” they will eventually be asked to leave the community or will not feel entitled to ask for community support. Often it is the workers who are most vulnerable—due to gender, class, or ethnic positions—who are unable to forgo time and income for the sake of the community.

Response: Similarly, in Bengaluru, the handful of female food delivery workers cannot enjoy or bank on support from the mostly male app workers’ “brotherhood” networks. The same dynamic plays out in Mumbai. Female workers have a starkly different relationship with cities across South Asia from those of their male counterparts. A lack of clean public toilets, unsafe neighborhoods after dark, and rampant casual harassment on the streets make it harder to work profitably. This is compounded by the fact that app-based demand for food and services rises at the most inconvenient and dangerous hours of the day and night. Relying on male workers’ help also means risking overfamiliarity and the possibility of unwanted attention. Other workers in these cities don’t fit into the identities along which unions and other formal worker associations are organized, or don’t feel a need to organize formally because they already feel embedded in informal networks of kinship.  Many drivers in Jakarta and Bengaluru often do not want to actively agitate for better conditions because they believe that gig work is the best job they are ever likely to have and fear losing it. Other drivers consider gig work as a temporary stepping stone to other jobs, education, or enterprise—becoming street vendors, for example, or opening their own cornershop. Whether and how these workers choose to resist their working conditions is thus shaped by an awareness of their alternatives, or a lack thereof. (The sociologists Rina Agarwala and Jennifer Jihye Chun have shown how the specific circumstances of informal work shape labor organization opportunities.) For many workers in South Asia, these alternatives are not unionized or formalized employment, but more subcontracted, informal labor, such as domestic or construction work, or jobs in small informal businesses where accountability is diluted by brokers between the final “employer” and the workers with few formal contracts.


Prompt: A Continuum of Strategies For many people in the Global South, work has long been isolating and uncertain by design. As “low-tech” workers such as platform drivers build community and collective power, they are able to draw on different local histories of resistance, and different methods for negotiating the social and political tensions in their cities.

Response: Often, in the analysis of gig worker power by academics and observers in the Global North, an absence of unionization is thought to indicate an absence of worker power. Unions in the Global South, though, are not seen as the only or best way to collectivize in these labor regimes. This is not to argue that workers in the Global South do not unionize or that unions are unhelpful. Rather, they exist on a continuum of strategies to reshape work conditions, build collective worker identity and engage in mutual aid. (The political economists Arianna Tassinari, Matteo Rizzo, and Maurizio Atzeni, among other scholars, have examined in depth the role of unions in precarious work conditions.) Recognizing why alternate modes of organizing exist, and who uses them tactically, may be key to keeping tech labor movements around the world inclusive and responsive to the needs, vulnerabilities, and politics of those who do not have the privilege of participating in visible direct action. To achieve solidarity, and to succeed, the movement must engage with the informality, power asymmetries, and inequities of class and caste that shape the conditions of work around the world.


Prompt: For almost as long as there have been digital technologies, there have been critiques of digital technologies. One of the oldest and most consistent concerns associated with computation is “datafication.” Datafication turns the activity of people online (and, increasingly, offline) into data, that all-important resource that allows companies to discover patterns in our behavior and exploit them for commercial gain.  Consider a simple example. If I walk into the café across the street and buy a coffee using the store’s Square payment system, this transaction is datafied. Information about what kind of coffee I bought, how much I paid, and what time I bought it is gathered by Square and added to the dossier of all my past purchases and the dossier of all the café’s past sales. By datafying this transaction, Square can learn about my consumption patterns and about the café’s business. Square may then use these lessons for commercial gain: for instance, by selling information about coffee purchasing activity in my neighborhood, or by developing risk profiles for commercial loans to coffee shops.  Datafication is a basic part of what makes the digital economy digital; it also features prominently in critiques of the digital economy. But what makes datafication wrong, exactly? Currently, there are two main lines of thought. First, datafication is a form of surveillance that violates our autonomy by undermining the ways in which we develop our sense of ourselves and express those selves in the world. Second, datafication is like feudalism—it traps us in unfair economic arrangements in which we provide our valuable “data assets” or “data work” for free by using the digital services created by tech companies.  Both of these perspectives provide valuable insights into why we should care about datafication. Yet they also have serious limitations. In the digital economy, data isn’t collected solely because of what it reveals about us as individuals. Rather, data is valuable primarily because of how it can be aggregated and processed to reveal things (and inform actions) about groups of people. Datafication, in other words, is a social process, not a personal one. Further, it is a process that operates through a set of relationships. Only by highlighting the collective and relational character of datafication can we understand how it works, and the particular injustices that it produces. This isn’t just a theoretical exercise—it goes to the heart of what’s wrong with our digital world, and what may make it right.

Response: Autoplay in the Panopticon Traditionally, critiques of datafication have focused on the kinds of violations we identify with surveillance. This suggests datafication is wrong because it violates our “right to be let alone,” to quote Louis Brandeis, the father of US privacy law. Surveillance is a fundamental transgression because it undermines our ability to form autonomous selves.


Prompt: This account of what makes datafication wrong has its popular origins in Michel Foucault’s analysis of the panopticon. The original panopticon was a blueprint designed by the eighteenth-century philosopher Jeremy Bentham. It consisted of a cylindrical prison with a guard tower in the central courtyard. The prisoners could only see the guard tower, while the guard had a view into every prisoner’s cell. While this model was never adopted during Bentham’s lifetime, Foucault used the panopticon as a metaphor for the “political technology” that undergirds the modern disciplinary society. The panopticon’s design structures a relation of power through surveillance: an “unequal gaze” between the all-seeing observer and the observed.  In this view, surveillance, whether it takes the form of a prison or a dataflow, makes subjects legible to observers, and therefore more controllable. In the panopticon, the prisoner never knows for sure if she is being watched. So she must assume she is, with the result that she begins to police her own behavior and to internalize the goals of the prison guard as her own. In other words, surveillance technologies don’t just observe us. They can also change how we act.  Foucault was primarily interested in how the state used surveillance to produce docile and predictable citizens. More recently, however, large tech firms have been using surveillance to produce predictable and profitable consumers. Netflix’s autoplay feature is a good example. For Netflix, one of the most important indicators of user engagement—and, by proxy, customer satisfaction—is watch time: how many hours someone spends on the site. In order to maximize watch time, Netflix ran a series of tests that involved tweaking its interface in various ways to see which changes resulted in more watch time from its users. These tests showed that the best way to increase user engagement was to automatically begin playing the next episode. Netflix’s granular, data-intensive analysis even suggested the most effective “wait time” before the next episode played: seven seconds.

Response: Through surveillance, Netflix has found a way to modify TV-watching behavior with its own goals in mind. The site nudges a user into doing something that feels good in the moment, but that may go against her better judgment and her own will, if only she had a few seconds more to think about it.  Strengthening privacy rules could mitigate such concerns. Such rules might require companies to obtain meaningful (not coerced) consent or give data subjects more control over how their data is used. These expanded rights would reassert the authority of the individual’s will against the manipulative machinations of technology companies, restoring the autonomy that has been destroyed by datafication.


Prompt: Show Me the Money Datafication doesn’t just involve observing and manipulating individuals, however. It also involves monetizing the data that is collected from them. This brings us to another critique of datafication. If one camp argues that surveillance erodes our autonomy, another emphasizes the unfairness of companies getting rich from our data. The same techniques that influence user behavior also generate wealth for tech companies, thus contributing to growing economic inequality. The data-intensive practices of companies like Google, Amazon, and Facebook have made them and their CEOs fabulously rich, in great part due to how these companies exploit the insights they can gain from their vast stores of user data.  One solution is to pay people for their data. If datafication is wrong because of how it helps companies make money from users without compensating them, then forcing those companies to compensate their users could right this wrong. Gavin Newsom, Andrew Yang, and Alexandria Ocasio-Cortez have all expressed support for the idea. And the proposal gets at something crucial about datafication missing from the surveillance-centered critique: the stark distributive realities of the digital economy.  The pandemic has brought unemployment and hardship to many but has been a boon to tech monopolies. Jeff Bezos’ wealth grew by more than $70 billion in 2020. Paying people for the data they supply to tech companies is one way to recapture and redistribute some of this prosperity. It would spread the wealth made through datafication more evenly among all of those who helped create it.

Response: Relations, Not Things Both the “autonomy” and the “nonpayment” accounts of datafication make valuable points. But they also miss essential aspects of how datafication works and what makes it wrong. It’s true that datafication can impede our free will. However, an emphasis on personal autonomy can obscure the extent to which the stakes of data collection extend far beyond the individual data subject. It’s equally true that datafication contributes to inequality. But paying individuals for their data won’t eliminate that inequality because it doesn’t address the broader conditions that can make datafication coercive.  In an increasingly digital world, datafication is the material process whereby a great deal of injustice happens. Through datafication, we are drafted into the project of one another’s oppression. This is not a personal process, but a social one. Datafication operates through a set of relationships. There are the relationships between us and the executives and investors who enrich themselves by surveilling us. There are also the relationships inscribed by racism, sexism, xenophobia, and other group-based oppressions, which datafication manifests in new digital forms.


Prompt: These “data relations” are the digital expression of the growing gap between tech’s winners and losers: those who predominantly benefit from datafication’s personalization and efficiency, and those who disproportionately bear its risks. Sometimes these gaps are economic, like those between platform CEOs and the gig workers who make them rich. Other times they are social, like those between Amazon Ring homeowners who enjoy being able to make sure their packages are safe, and passersby that the Ring subjects to heightened risk of violent police encounters. In both cases, data relations materialize relationships of domination between different groups of people.  A recent story by Joseph Cox for Vice offers an especially dramatic example of this dynamic. According to Cox’s reporting, the US military is purchasing granular location data from a popular Muslim prayer app with over 98 million downloads worldwide. The app, Muslim Pro, pings its users five times a day with reminders to pray, uses a compass to orient them toward the Kaaba in Mecca, helps them observe fasting during Ramadan, and guides them to halal food in their area. These features have helped turned Muslim Pro into the most popular Muslim app in the world, according to its maker, Bitsmedia. It has also meant that the app collects vast quantities of detailed location data.

Response: The revelation that this data is now in the hands of the US military has sparked dismay and condemnation in the Muslim community. But what makes this instance of datafication wrong? Under the autonomy account, the dataflow from a user to Muslim Pro to the US military is wrong because the user is being surveilled by a government whose presence in their religious life may have a chilling effect on their religious activities. Had the user been notified or given the option to opt out of this sale, they would almost certainly have done so. Under the nonpayment account, on the other hand, this dataflow is wrong because users are not being paid by the app or the US military for their data. If the sale of user data is making Muslim Pro money, users deserve some portion of that profit.  Both of these analyses may be partially true, but both are unsatisfactory. Imam Omar Suleiman, a prominent Muslim scholar and the founder of Yaqeen Institute for Islamic Research, highlights the importance of the broader social and historical context for understanding what makes the Muslim Pro situation particularly harmful. “This is not taking place in a vacuum,” Suleiman told the Los Angeles Times. He notes the long pattern of government “violations of our civil liberties that have preyed on our most basic functions as Muslims”—a pattern that includes not only intensive domestic surveillance, but also the expansion of the drone program under President Obama, which resulted in hundreds of civilian deaths in Muslim-majority countries.  The responses from Muslim community leaders reflect the social—not merely individual—significance of the dataflow between Muslim Pro and the US military. For them, this instance of datafication is wrong because it drafts users—faithful Muslims—into the project of their fellow Muslims’ oppression. It takes a digital means for expressing and enacting their faith and transforms it into a vector for potential violence, materializing the imperial relation between Muslims and the US state. Datafication is a fundamentally collective and relational process, and so are the injustices that it inflicts.


Prompt: Asking the Right Questions Defining datafication in this way doesn’t just present us with a more complete picture of how the process works. It also gives us a way to make datafication more just. Like analog social relations, our data relations may be oppressive, exploitative, and even violent. But also like analog social relations, our data relations may be empowering and supportive and may act as a moral magnifier—enabling us to achieve goals together that we could not accomplish alone.  Thinking of datafication as the digital terms by which we relate to one another clarifies the kind of political interventions that are required. The point is not to define the terms of our individual datafication—by demanding our share of the pie, or shoring up resistance to being rendered legible against our will—but to define the terms of our collective datafication. We may choose to define those terms in ways that contribute to greater equality. For instance, we might apply datafication toward retrieving spheres of life from market governance. Aaron Benanav has described the role that digital infrastructures could play in democratizing production and allocation decisions by substituting the information generated through datafication for price signals. Other urgent public tasks may also require datafication. A detailed accounting of our individual and collective use of natural resources will be increasingly necessary for the efficient and fair allocation of these resources as our environment undergoes accelerating climate change.  What kinds of projects are worth being drafted into? What forms of relating to one another are just? And how do we begin to develop the institutions through which the democratic negotiations of our shared priorities can be facilitated? A theory of what makes datafication wrong can’t provide us with the substantive, and essentially political, answers to those questions. But it can clarify which questions need answering in order to achieve a more democratic future.

Response: The geneticist Steve Horvath likes to recall a pact he made with his identical twin at the end of high school in the late 1980s—they would dedicate their careers to extending the human lifespan. Horvath stuck to his word. After getting degrees in mathematics and biology, he started building statistical models hoping to answer an intimate, terrifying question: how long do we each have to live before we die? Horvath began his work on that question in the mid-2000s, as a professor at the University of California, Los Angeles. He was working in a relatively new field called epigenetics, the study of how biological processes alter gene expression without changing our underlying DNA. If you imagine DNA as a ribbon of patterned cloth, epigenetic processes can be thought of as a sort of embroidery that accentuates or conceals its design.  To illustrate the power of epigenetics, some scientists point to the differences among females in the colonies of certain ant species, including Camponotus floridanus, the Florida carpenter ant. All the females in a floridanus colony share the exact same set of genes, but their behaviors and lifespans are dramatically different. Some serve as foragers, others as soldiers, living about a year and never reproducing; only one ant rules as queen, laying eggs and living for up to thirty years.  Horvath began looking for epigenetic processes that might correlate with aging in humans. One was a process called DNA methylation, in which small molecules of carbon and hydrogen attach to particular parts of a person’s genetic code, with the ability to activate or suppress the underlying genes. In 2011, Horvath measured methylation in DNA from saliva samples and found that the more cells a person had with a particular methylation pattern at three DNA sites, the older a person tended to be. The sample was small, but the correlation was strong. DNA methylation predicted age to within five years in more than 85 percent of the subjects.


Prompt: To find out how robust this relationship was, Horvath began gathering publicly available datasets that included information about subjects’ age and DNA methylation patterns. By 2012, he had gathered eighty-two datasets with eight thousand samples in them. The data came from a wide range of people of different ages, and from different parts of the body: cord blood from newborns in various parts of the world; brain, stomach, lung, liver, breast, and uterine tissue; sperm; immortalized B cells from people with a rare genetic disease. He even gathered data on chimpanzees.  Horvath used about half of these datasets to train an algorithm to look for associations between DNA methylation and age. Then he tested the algorithm on the other half of the datasets. What he found was so remarkable that several academic journals rejected his results out of hand. The pattern of methylation at just 353 of the three billion or so pairs of DNA nucleotides in the human genome corresponded to a person’s age with 96 percent accuracy, an unprecedented degree of correlation between a biomarker and the process of aging. After Horvath finally convinced a journal to publish his results, other researchers began to replicate them.  Horvath referred to his successful algorithm as an “epigenetic clock”: insert some body tissue, and it would spit out your epigenetic age. He hypothesized that people with a higher epigenetic age than chronological age (the one we mark with birthdays and greeting cards) might be at a higher risk of early death. With a group of fellow researchers, he set out to develop a new algorithm that would analyze methylation patterns at DNA sites associated with mortality and compare them to the age of death of subjects in large longitudinal studies. It turned out that the 5 percent of people with the highest epigenetic age relative to their chronological age were twice as likely to die prematurely as an average person of their chronological age. Horvath and his colleagues called this new epigenetic clock GrimAge, after the grim reaper, because it seemed to have the disturbing potential to tell if you were headed for a premature death.

Response: This was a scientific breakthrough with an uncommonly wide range of applications, some of them quite unsettling. Almost as soon as Horvath began publishing the research on his clocks, people began to realize their potential to shape domains from law enforcement to healthcare. Scientists started to investigate whether the algorithms could be used to help solve crimes by using genetic material left at crime scenes—blood, hair, skin cells, bodily fluids—to determine the age of unknown victims or perpetrators. Online, a half-dozen or more companies sprang up selling versions of Horvath’s clocks for around $200, promising prophetic insights to largely affluent customers concerned with prolonging their youth. These companies are like 23andMe, but instead of purporting to reveal your ancestral past, they claim to divine, and allow you to intervene in, your biological future.


Prompt: This rush to commercialize epigenetic clocks involves a number of potential pitfalls. Some industries are likely to use the clocks to wittingly or unwittingly entrench various forms of social inequality. Other commercial applications of the clock are based on a fundamental and possibly willful misunderstanding of the science that shifts responsibility for social and structural problems on to individuals. At the same time, this commercialization obscures something more hopeful: the democratic potential of the clocks.

Response: Distributing Death The industry that has so far taken the largest interest in epigenetic clocks—and where the dangers of those clocks are most clear—is the four-trillion-dollar-a-year life insurance industry. Life insurance offers an important form of financial security to the heirs of people who can afford it. For example, if I die before paying off my mortgage, life insurance might mean my kids can still keep a roof over their heads.


Prompt: The industry’s business model involves using troves of personal and population-level data to bet on how long individuals will live, and charge them accordingly. Underwriters collect data from applicants on things like personal and family medical history, occupation, lifestyle and hobbies, and then gather data on the backend from driving records, criminal records, prescription history, credit reports, and more. Underwriters feed a portion of this data into an algorithm, which helps them determine a risk score that measures how likely an applicant is to die during the term of the policy.  You pay for the risk you represent, no matter its source. This principle is known in insurance as “actuarial fairness.” Those who are young and healthy can get more financial protection for less money, while the older or more at risk of death you are deemed to be, the more expensive and less accessible that protection becomes. Maybe you’re a wealthy suburbanite who likes racing ATVs on the weekend, or a formerly incarcerated person who now works on a fracking rig, or a single mother with asthma living near a superfund site—all of this helps determine your risk score and how much insurance you can access. Life insurance, in short, is a system built on discrimination.  A reliable test for how one’s cells are aging could be a transformative new tool for the industry. It’s illegal to explicitly discriminate against applicants on the basis of race, gender, or other categories protected by the law—life insurance companies used to deny coverage to Black people and women, but allowed masters to insure their slaves—but epigenetic underwriting could make such discrimination possible through a new form of algorithmic redlining. That’s because epigenetic tests can measure chronic environmental and psychological stresses that often map on to race, class, and gender. For example, one study used one of Horvath’s epigenetic clocks to examine blood samples from 392 Black adults and found that high lifetime stress correlated with accelerated epigenetic aging. Similarly, populations living in highly polluted areas, oftentimes the poor, will have distinctive methylation patterns that could allow the life insurance industry to further discriminate based on class.

Response: There’s a second way that epigenetic tests are likely to be used in the life insurance industry, one that carries a related risk. Epigenetics has been heralded within both academia and the media as the answer to the longstanding nature-versus-nurture debate: proof that our surroundings and behaviors influence us on a molecular level. Using this logic, the life insurance industry—along with companies offering consumer epigenetic tests—is likely to use epigenetic clocks to offer personalized health insights and behavior-modification programs to its customers. High epigenetic age? Trying eating more kale! This could also lead to discount programs that reward policyholders who successfully lower their epigenetic age. Similar discount schemes are commonplace in auto insurance, where companies like TrueMotion allow policyholders to download an app that tracks their actions behind the wheel and gives discounts or makes price hikes based on safe or risky driving.


Prompt: The problem with this, of course, is that nurture is nothing like driving. Nurture is the air quality and poverty levels in the neighborhood you grew up in; where you went to school and what you ate for lunch; the way you were treated by your parents and society; the life stresses you have experienced. Like aging processes, all of this is gendered, classed, and racialized. Despite what the life insurance and other industries might claim about personalized behavior-modification programs based on epigenetic tests, you can’t meditate your way out of structural inequality. But this misunderstanding about the nature of nurture is useful to such industries—it generates profits by shifting risk and responsibility for wellbeing from societies, corporations, and governments onto individuals.  That’s not to say nothing is under our control. Some things surely are, like diet and smoking. Be that as it may, we have very little idea how precisely these choices affect epigenetic processes of aging. A 2016 study by Brian Chen, a scientist who worked with Steve Horvath and is now at a life sciences company that is trying to pioneer epigenetic underwriting, found that the correlation between early mortality and methylation remained even after controlling for lifestyle factors such as exercise levels and smoking status. This suggests that epigenetic clocks capture some aspect of biological aging that is divorced from—and can’t be altered by—lifestyle. As Chen recently told me, “We don’t know, as the scientific community, because the science is so new, what will alter one’s epigenetic clock.” Proof of Harm Though our genes differ, shared experiences and circumstances shape us in similar ways. The life insurance industry may want to use epigenetic tests in a manner that entrenches inequality and pernicious myths of personal responsibility, but the science can also be used to strengthen the case for action on social and systemic problems.

Response: “The health impact of environmental and social inequalities, phenomena that occur outside the body, is now identifiable, measurable, and potentially treatable within the body,” Charles Dupras, a bioethicist at the University of Montreal, has written. There are many situations in which molecular-level proof of harm could bolster advocacy work and the case for regulations. For example, multiple existing environmental policies—such as the Clean Air Act, the Safe Drinking Water Act, and the Toxic Substances Control Act—require agencies to assess whether an atmosphere harms human health, and epigenetic tests could be an important source of proof.  Epigenetic tests could also lead to safer working environments. Under the Occupational Safety and Health Act, the cornerstone federal law regulating workplace safety, employees are entitled to a workplace free from “recognized hazards that are causing or are likely to cause death or serious physical harm.” Mark Rothstein, the Director of the Institute for Bioethics, Health Policy and Law at the University of Louisville School of Medicine, argues that epigenetic tests could capture the adverse effects of everything from toxins at work to a hostile work environment.


Prompt: When I asked Dupras why he believes that we need epigenetics to promote findings we already know to be true—pollution is bad for your health, exercise can help you stay healthy—he said that epigenetics meets the needs of both the social scientist pushing for equitable policy and the biotech company looking to make advances in precision medicine. Sometimes these things seem at odds—we hunt for cellular therapies when we should be working to change the environmental conditions that cause illness in the first place—but there’s no reason epigenetic science can’t help us do both.  As the field matures and we learn more about how our environment impacts our biology, we should be skeptical of market solutions promising individualized interventions. As consumers, we should recognize epigenetic testing for what it is: exciting molecular confirmation of the fact that healthy environments promote longer lives. As a society, we should acknowledge it for what it could be: a discovery that will help us to draw roadmaps toward a better collective future.

Response: In 2013, a series of posters started appearing across Washington, DC, that declared, “The Internet: Your Future Depends on It,” next to a photo of a Black Washingtonian. “Sean earned an advanced certification in six months. Now he upgrades computer systems for the US Small Business Administration. He uses technology to help people start businesses. So can you.” The people in the posters looked ahead to their new future, smiling. “Fabiane learned Microsoft Office in eight weeks and used her new skills to write, design, and publish her first book. She’s using technology to pursue her dreams. So can you.” The posters told stories about using digital training resources provided by the DC municipal government to get to such futures. New skills and tools would lead to better jobs, ones in which you don’t work with your hands. But it was not just a matter of bringing single individuals across a digital divide, a gap between those who had internet access and the skills to use it and those who didn’t. The dream was bigger: by changing their tools and skills, people could drive the economic growth of the city and change the communities in which they lived. “Marcus earned three computer certifications in less than a year. Now he works as a computer technician with the DC government. He uses technology to improve his city.” Curiously, the internet itself wasn’t mentioned in these testimonies. No one was designing websites or setting up e-commerce portals. The internet in “The Internet: Your Future Depends on It” was not a specific tool, but a symbol of economic progress—the promised land people would reach with the right equipment and the right training.  There is a familiar, attractive story here: get online, learn to code, secure your future. Both liberal and conservative politicians have repeated that story for decades, updating it for the technology of the day, in an attempt to persuade the public that their individual and collective economic futures depend on their access to the right skills and tools. The narrative is so pervasive that it has become political common sense.


Prompt: I call that political common sense the “access doctrine.” The access doctrine decrees that the problem of poverty can be solved through the provision of new technologies and technical skills, giving those left out of the information economy the chance to catch up and compete. Like other forms of political common sense, the access doctrine mixes factual claims with ideological ones. It is clear, for example, that finding a job without an internet connection and a PC is difficult—just try filling out an application to work for CVS, let alone using USAJobs, on your phone. It is less clear that there are plenty of good tech jobs out there, just not enough coders to fill them. Economic reality is more complicated than political slogans might admit. Getting online and learning to code won’t change the rest of the labor market by itself.  What’s more, the access doctrine implies a certain calculus for social worth: your social value diminishes alongside your economic value. Inequality is a feature of a capitalist economy, not a bug, and the access doctrine makes this inequality sensible and navigable. It explains why there is such a gulf between rich and poor, how the poor can find security, and what help they need to get there—all without disrupting the basic shape of these unequal social relations.

Response: Before Tomorrow Arrives The access doctrine is part of a wider story that explains the relative poverty or economic inactivity of specific populations, regions, and countries through the skills that individual workers possess. For much of the twentieth century, especially after the economic dislocations of the 1970s, representatives of business, education, and government have warned of impending or actual skills gaps, where the education system does not provide graduates with the skills businesses need, and the more specific problem of skills shortages, where US businesses cannot find a specific kind of worker—today, generally engineers and information technology professionals.  Skill is notoriously difficult to define and measure. Nevertheless, the urgent problem of skills gaps and shortages has been given historical weight through the invocation, typically by economists, of skill-biased technological change (SBTC). Advocates for SBTC hold that the prevalence of technology—itself typically unmeasured and underdefined—increases in prevalence and complexity over time, and thus the demands for and wage returns of workers skilled in its design and use also rise over time. The vagueness of skill and technology are empirically troublesome but politically useful. A nebulous threat is always on the economic horizon, explaining that those struggling today do so because they have not sufficiently upgraded—and that they must do so before tomorrow arrives.


Prompt: The skills-gap narrative shifts the responsibility for training away from business owners and toward young individuals at the beginning of their working lives, as well as toward the public institutions that train them: universities, community colleges, and local and state governments. As management researcher Peter Cappelli has shown, what little evidence we have suggests that employers today devote very little time to on-the-job training relative to the postwar “golden era” of long-term, single-firm employment.  Even the evidence for the existence of a skills gap is at best mixed, as is the evidence of positive labor market returns, for individuals or regions, from attempts to “skill up” local workers or import new ones from other parts of the country. Most STEM degree holders work in non-STEM fields—in part because firms are able to outsource high-skilled labor to low-wage workers through digital networks or import them from abroad through programs like the H-1B visa system. The Bureau of Labor Statistics consistently projects that most future jobs will be in low-wage service work like home healthcare and food preparation, not high-wage knowledge work. For most workers, the jobs of the future rely on catheters and cutting boards, work that requires no knowledge of Python or JavaScript and often no more than a high school education.

Response: But still, the hope survives. At the level of statecraft, placing a political priority on skills training is a positive, forward-looking way to justify cuts to unemployment insurance—because it’s unnecessary if there really are enough jobs available for the skilled—and the general shift of training burdens from firms to schools, libraries, and individuals.  The access doctrine emerged from these earlier frameworks, adding to them the new technologies and skills associated with the internet. It was first articulated in debates in the 1990s over the problem of persistent poverty in a globalizing, deindustrializing US economy whose most profitable frontier seemed to be in using and producing information and communication technologies. In the following decade, in cities such as DC, Seattle, and San Francisco, the technology sector expanded as part of a wave of post-2008 high-wage, white emigration, while Black unemployment remained persistently high, homelessness increased, and working-class wages barely budged. The access doctrine made these problems appear natural and immutable, like an earthquake, and taught individuals and organizations how to survive them.  These problems are complex, but the access doctrine continues to make sense as a simple solution. It makes it seem as though economic opportunities are available to all via the internet, and thus any individual economic struggles are just that—and never the fault of deindustrialization, capital flight, stagnant wages, or a shrunken, punitive welfare state. This is part of the neoliberal revolution, wherein American political institutions have redefined citizenship around market fitness and charged the state with either ensuring opportunities for competition and broad participation in them, or policing those who cannot or will not compete, so as to preserve the smooth functioning of markets. The threat of punishment, of course, falls most heavily on the working and workless poor, particularly Black Americans.


Prompt: Bootstrapping As the skyrocketing inequality of the information economy has become harder and harder to ignore, the access doctrine, manifested in stories like “The Internet: Your Future Depends on It,” has become more and more compelling both to individuals and to institutions. In the process of transforming the problem of poverty into a problem of technology, the access doctrine has transformed the organizations—schools, libraries, governments—addressing the problem.  Schools and libraries, threatened by fiscal austerity or accusations of obsolescence, have embraced the access doctrine as their mission in order to restore their legitimacy, secure much-needed resources, and simplify the host of social problems with which they are confronted daily. They do so because public service organizations are themselves under threat in an environment of overwhelming economic uncertainty and inequality. By turning the problem of poverty into a problem of technology, they reframe their own problems into something more manageable for their frontline staff and more legible to the politicians, donors, and others who might offer support. These institutions teach us how to survive, but their own survival hinges on reproducing the common sense of the access doctrine. In the process, places like schools and libraries begin to look more like tech startups.

Response: I call the process of organizational restructuring prompted by the access doctrine “bootstrapping”: public service organizations are overwhelmed by the scale of the problems facing them and find their resources and legitimacy under threat, so they turn toward technology provisioning and skills-training programs because these attract economic and political support. When schools and libraries bootstrap, they are often inspired by the way technology startups pivot to new growth models. Ultimately, though, they cannot pivot like startups, because they have different goals, stakeholders, revenue streams, and responsibilities.


Prompt: Because bootstrapping empowers public service organizations that manage the problem of poverty, it is a never-ending process. Any part of the organization’s identity, operations, and personnel are subject to revision. That is why libraries increasingly look like Apple stores. Or why urban charter schools promise a brighter future for working-class students of color if they just learn to code, even if they won’t receive a warm welcome for the relatively small number of jobs in software development. Bootstrapping becomes not just a series of changes, but a new institutional culture, in conflict with older public service cultures. Paradoxically, it is precisely because the access doctrine presents such an urgent problem for these organizations that they will continue with new and different experiments in technology provision or skills training—even if those experiments do not benefit poor people, or marginalize the people the organization serves. Indeed, bootstrapping regularly fails on its own terms, because the institutions it targets are not made for the task.  Still, bootstrapping institutions reproduce the access doctrine, and thus the idea of different sides of a digital divide—with one in need of the other’s help—as part of their general task of reproducing people for capitalism. It is insidious work because the ideal subject those institutions are redesigned to reproduce is an entrepreneur who has no need for schools and libraries; they can learn by themselves, work by themselves, start a tech company by themselves and weather extreme economic uncertainty by themselves.

Response: A New Hegemonic Bloc Most accounts of the information economy focus on changes in the mode of production—the nature of work, what commodities are produced and where. But there has been just as large a change in the mode of social reproduction, the differentiated process of making people for a particular political-economic moment.  Making the problem of poverty a problem of technology changed how we understand the entire labor market, our navigation of it, and the reward from it—for employed, unemployed, and underemployed alike. The hope that personal computing, the internet, and the skills to use them will power social mobility is the cultural glue holding a deeply unequal information economy together. In this new mode of social reproduction, no one is ever truly on the outside of the labor market—everyone is constantly searching for the skills, technologies, and opportunities that will help them move through it. This is a feature, not a bug, in neoliberal economic development.  Capital cannot make people, but its need for more and different labor power is balanced by an impulse to disinvest from the costs of social reproduction and disrupt or abandon the spaces of solidarity and community that are grown therein. The political philosopher Nancy Fraser argues that this dynamic creates a “crisis of care” in each capitalist epoch. Our current crisis is marked by the state’s retreat from the responsibilities of care and the stagnation of wages for much of the working class, leading to a “dualized organization of social reproduction, commodified for those who can pay for it, privatized for those who cannot—all glossed by the even more modern ideal of the ‘two-earner family.’”  I would add, following the radical geographer Ruth Wilson Gilmore, that the state has not so much retreated from care but shifted those capacities into punishment—particularly for the working and workless poor—and that this is complemented by capital’s retreat from providing training within the firm. Capital has shifted the burden of skills training onto individuals so that they must now have all the skills necessary to succeed at jobs they do not yet have. To carry the burden of skills training, individuals take on debt, access to which is also racially differentiated, in order to pay for college or coding boot camps. If they are on the fringes of the labor market, they are forced to enter a constant cycle of job search, job applications, and skills training—the replacement of welfare with workfare. Police and prisons are tasked with handling those individuals who will not or cannot shoulder these burdens.


Prompt: The ideal economic model for individuals at this juncture is the tech entrepreneur, just as the ideal model for organizations is the tech startup. The ideal entrepreneur is, as the sociologist Tressie McMillan Cottom puts it, a “roaming autodidact” who seeks out the unlimited educational opportunities available on the internet. They are self-sufficient, not reliant on institutions of social reproduction. The ideal entrepreneur is a young white male who not only lacks any responsibility to care for others, but also has his own care needs commodified, provided either by an on-demand delivery app or by his workplace.

Response: The contemporary shift in the mode of social reproduction is based on making more of these ideal entrepreneurs, not necessarily because we need them, but because they supposedly thrive in an environment of privatized care and general macroeconomic uncertainty. The access doctrine secures consent to those conditions. By turning the problem of poverty into a problem of technology, economic security becomes a matter of getting the right tools and the right skills. Individuals must seek out these resources for themselves, and institutions can only survive by assisting them.


Prompt: The function of the access doctrine, then, is not just in its ability to persuade the masses, but in its ability to mobilize different classes and different institutions into a new hegemonic bloc. Hegemony, Antonio Gramsci held, is the substitution of one class’s interest for the rest of a society’s, secured through alliances with and concessions to other classes. Ford’s five-dollars-a-day wage is the classic example: high for its time, but necessary to guarantee participation in the violent grind of the assembly line.  Capitalists cannot simply speak hegemony into being. The access doctrine persists in part through an alliance between the two sections of the professional-managerial class: those reproducing capitalist relations within the firm (such as software engineers) and those doing the work of social reproduction outside the market (helping professionals at places like libraries and schools). Remaking a city in tech’s image requires both an influx of new tech workers from the outside and the transformation of the existing populace in support of that new project.  But unlike those members of the professional-managerial class, who are isolated from the working class they manage, teachers, librarians, and the like are, in the course of their everyday duties, directly confronted with the dual assault on both their own organizations and the livelihoods of the people they serve. In the Washington, DC library and school systems, which I explore in my book The Promise of Access: Technology, Inequality, and the Political Economy of Hope, that class consciousness has led to, at best, ambiguity. But other schools, libraries, and cities are possible.

Response: Sites of Struggle The power of the access doctrine resists refutation. Politically, it’s ultimately unfalsifiable. Even if today’s training regime does not work, new technologies will always arrive and, for those interested in shifting the risk of economic transition to individual workers, they will always demand new skills. But understanding its role in social reproduction is an important first step in any attempt to act on that sphere of the political economy and, perhaps, to rebuild it in a shape more conducive to human flourishing.


Prompt: Such a movement will require a broad, organized assault on the neoliberal political apparatus. Had I the recipe for such a program, I would gladly share it. However, I am confident that our economy can be, if not remade, at least equalized, and the bootstrapping cycle broken by the people within these institutions.  This will require a split within the professional-managerial class. So-called “helping professionals” must break with the tech workers with whom they shared college dorms and now share apartment buildings. Instead, they must organize at the point of reproduction in solidarity with their patrons and students, based on the recognition that both helper and helped share an interest in preserving the institutions of public service threatened by the access doctrine.

Response: Strategically, both sides need each other. The helping professionals are few in number, and state and capital can easily paint their protests as a dereliction of sacred duties (e.g., “teacher strikes hurt kids”). They need support from the communities they serve in order to stand up to this political pressure. On the other hand, students, patrons, and their communities have numbers, but lack the strategic position within the mode of reproduction that helpers’ work provides. A protest in support of library patrons is one thing, but a library shut down by its workers is another thing entirely—revealing just how much the city relies on those institutions to function and the sort of power those professionals have. That work is already happening across the country. The Chicago Teachers Union led the way in 2012 (and again in 2019), organizing teachers and parents alongside workers. Recent teacher strikes across the country have used the same tactics to join struggles against school privatization and school policing with teachers’ fights for better jobs and better pay.


Prompt: Centers of social reproduction like schools and libraries are not merely victims of neoliberal reform, but sites of struggle in which counter-movements can be built. The story driving so much of our thinking about poverty—“The Internet: Your Future Depends on It”—did not appear out of thin air. It had to be told over and over, reinforced through computer certifications, progress reports, and planning documents. We built these coping strategies to make overwhelming economic inequality sensible and navigable. But if access today fundamentally means an opportunity to compete, then an alternative should not be so hard to imagine. If the world as it currently exists is one where we must be granted the tools necessary to strive for excellence, to innovate beyond our current dire straits, and to outcompete inequality, then surely another world is possible—one where innovation is boring and excellence is unnecessary, because the good life is ordinary.

Response: Dr. Keolu Fox is a Kānaka Maoli (Native Hawaiian) genetic scientist, and professor at the University of California, San Diego. His multidisciplinary research includes genomic sequencing and editing, and Indigenizing medical research. While the history of genetics has been marred by notions of “normality”—such as Sir Francis Galton’s insistence on a “normal distribution” to justify eugenic ideas—Dr. Fox’s research is flipping the script on norms in genetic science. Many of his projects combine cutting edge technologies, Indigenous data sovereignty and futures, alongside questions of equity and how benefits of medical research are distributed. We sat down with Dr. Fox to learn more about what genetic data is, and the promises and pitfalls of genetic research.


Prompt: What is digital genetic data and how exactly is it used?  I hadn’t thought about digital genetic data until a professor by the name of Maynard Olson brought it up during medical school. When you enter graduate school, you have these retreats that you go on, which is very popular in medical school-ville. I was at a retreat and we were sitting around the fire, drinking whiskey with my department grandpa, Maynard Olson. Maynard is a cool cat. He asks me, “You know what the funny thing about genomics is, Keolu?”, and I was like, “No, uncle, what is it?” He replied, “We’re using technology to take something that’s analog and making it digital.” That led to this whole conversation about what bioinformatics is, where it comes from and what technologies enabled it to happen.  It really comes down to a few characters that really envisioned this. A lot of it happened at Caltech with this guy named Lee Hood. He, in my opinion, is the technologist that enabled the genomic and post-genomic revolution, as well as its evolution. He set out to create and automate a lot of these technologies and is probably the most influential person when it comes to that.  Think about this: a chemical composition that exists, and how we use technologies to literally take something that is chemical in nature—a pattern, a sequence that exists in nature—and how it can be translated into zeros and ones. Or as Uncle Maynard would say, zeros, ones, twos and threes: because AGCT, right? So then you’re now in this whole different world about the way we think about genes as a form of data and how it’s coded, mined, and operationalized.  In your lab, how are you using genetic data?  In a number of different ways—there’s certain things that you can do with it. You can read it—that is the sequencing modality, and there’s all of these different types of sequencing. There was the technology that Fred Sanger created early on in the ’70s and ’80s, which enabled Lee Hood to automate and create the ABI SOLiD, which enabled the Human Genome Project. From there, we have had new disruptive technologies from 2007 to 2010, and we get into this next-generation sequencing mode, and that is the birth of these technologies like Illumina, which is based here in La Jolla.

Response: That is a very strong reason why I work here—there are certain cities that are these technological epicenters, and La Jolla is one of those. It also has incredible surfing and great burritos and beer—and also has legal weed. So it makes sense that you’re going to get a critical mass of some really interesting characters in this dynamic ecosystem of people that want to innovate in the genetic technology space.  Sequencing is our primary tool, our bread and butter. But we don’t like to just read, we like to write, so we love genome editing and everything that’s come out of the back end of the genome editing revolution. The specific tools that we like to use include base editing or prime editing, and this comes from Alexis Komor, who is in the Department of Chemistry here at UCSD. Base editing is a precise form of genome editing that allows us to make individual nucleotide changes. So we can use both of those tools to do a bunch of cool shit.


Prompt: A new fundamental idea that’s been going on in our laboratory is to try to understand genetic data as a resource. We generate sequences from humans because we want to be able to predict and prevent disease—but then what is the actual value of that data? What exactly did you sequence? That brings us to another set of questions around diversity—not just as a buzzword, but as something that actually has immense value. What you sequence matters, because 90 percent or so of people that are sequenced are of Western European ancestry. The rest of the world, including Big Pharma, now knows that we’ve been looking in the wrong place.  How so?  Well, the most interesting things to me are throwback evolutionary questions. Evolution and natural selection, and what is medically actionable or not—these are not independent, they’re the same thing, right? Think about it this way. Darwin’s finches are this great example of natural selection because they allow us to understand variation in the context of geography. But so does sickle cell disease, and high elevation adaptation in Tibet and Nepal.  Now, let’s take it a step further. I can actually utilize that perspective and that understanding of natural selection to better inform the development of pharmaceutical drugs. As an example: women who are ancestral descendants of communities that have lived for 10,000 years at high elevation in the Himalayas have adapted and have mutations that are advantageous at that elevation—in something called the HIF pathway. That’s a really important mutation for understanding something like oxygen metabolism, and how we might develop new classes of drugs that are involved in cardiovascular disease, or different responses to Covid and respiratory health. Or the next Viagra.

Response: So you see how these are all just avenues for the development of these new drugs. And if I know that natural selection did all the work for me, it allows me to focus on a biomarker that I can then reverse engineer, and save myself a lot of money from the research and development point of view because I don’t have to try a million different isoforms. I can just zero right into the thing that I want.  When we find a genetic mutation that is associated with protecting against type 2 diabetes, that’s very interesting because might we be able to develop a new drug that is a competitor for metformin that classically doesn’t sometimes work in minority populations. Because on the back side of this, in 95 percent of pharmaceutical drugs, the clinical trials don’t include anybody except white people.


Prompt: DNA Equity For something like user data, Google collects data and makes it profitable. For genetic data, what’s the landscape of different actors like? Do you have open source datasets of genetic data, or is it mostly companies like 23andMe collecting genetic data? A timely question, given 23andMe’s decision to go public. Genomic data by itself is valuable, but it’s not as valuable as genomic data plus an electronic medical health record, or electronic data plus cholesterol data. It needs to have genotype and phenotype. So the metadata that’s associated with genomic data is super, super important.

Response: There is a lot of encouragement from the federal government, and obviously Silicon Valley and Seattle and DC to have an open data environment. Because once it’s all open, it can be aggregated and used to model and predict and prevent things, right? It’s a double-edged sword, because on the one hand you can see how effective an open data environment was in sharing a lot of the discoveries and insights from this last year of having a pandemic, and then how fast vaccines were developed. But there are always going to be perpetual linesteppers and companies that want to abuse this, right? Vertex Pharmaceuticals, for example, used genetic sequence data derived from cystic fibrosis patients, which they got from the nonprofit Cystic Fibrosis Foundation. Vertex incorporated and recruited patients through that avenue, sequenced their genomes, and then refined the development of this new drug—and then sold it back to those exact same people for $300,000 dollars a year.  To me, that’s super fucked up—I don’t care who you are. That’s a violation of the Common Rule, that’s a violation of the United States Constitution, that’s a violation of human rights. It’s also highly problematic because Vertex is actively taking advantage of loopholes that exist in our broken healthcare system, where it’s estimated that almost 60 million people don’t have access to healthcare. So, you know, it serves them to socially stratify and create this brave new world of health disparities, which is also gross.  These companies, they’re taking advantage of this—and not only taking advantage of it, they have lobbying groups to ensure that their assets stay their assets. It’s pretty dangerous. You see companies like 23andMe sell access to their database, a primitive version of their database to GlaxoSmithKline for $300 million. And then this year you watch Ancestry get sold to the Blackstone Group for $4.7 billion.  That’s a lot of money. It’s very obvious that the value of genomic data is on the rise. With 23andMe going public, you can see that it’s very obvious that the number one commodity on planet Earth is data, and digital sequencing information is a part of that apparatus.


Prompt: There are solutions, though, I should note. Give us some optimism.  So let’s be positive for a moment. It’s easy to point fingers at all of the terrible stuff that happens with capitalism, and we can get stuck in that kind of negative feedback loop really easily.  We have been thinking about different types of solutions. One of them would be for the example of 23andMe. For the people who gave you their genomic information for you to create this company, and you’re selling that information to Big Pharma, and then they’re developing drugs and selling it back to those same people for money—why not give them equity? Why not give them a stake? I feel like that’s a more sustainable model, financially. And would be a new cool kind of stockholder, shareholder-based benefit-sharing model. That’s the way we should be thinking about circular economies. What is just? And when we say equitable and you say equity, what do you mean? When I say equity, I’m talking about financial institutions and structures that allow us to move past the dollar sign and buy back our land. Something like stock shares in 23andMe would enable that, or trusts where that money can go to avoid corruption and cronyism.  In one of your papers, you talk about the “illusion of inclusion” and the NIH “All of Us” project. One of the common arguments about data is that if data is open instead of proprietary, it will benefit the public instead of just companies. But, in that paper, you talk about how we need to rethink what public benefit really means. Could you elaborate on that idea?  First, I would say, whose benefit? Whose greater good? I think that’s the bigger question. If we look at that historically, we know very well whose greater good we’re prioritizing over others, and we know that that’s quite hierarchical, right?  Couching it historically, what does it mean when the federal government uses your tax dollars to pay for a large-scale initiative to sequence one million people’s genomes in America, and that data is open? Does that mean that you, as an average US citizen, are going to benefit directly from that? You know, I think there are probably better uses of taxpayer dollars, maybe breaking up this multibillion dollar project into smaller grants and serving other basic scientific questions.

Response: But, in the meantime, the reason why this is problematic is because once that data hits the open market, it gets aggregated by these companies like Regeneron and others and is used in the development of pharmaceutical drugs. Meanwhile, the communities who graciously contributed their genomes to this, based on the false pretense of this having an impact on their health, will basically receive nothing.  Think about the history of the Human Genome Project from 2001. If we think about the actual impact of that project on health, it’s been kind of negligible with respect to common complex disease. What do I mean? The number one and number two cause of death in America are heart disease and cancer. If you look at the rates of the number of people in America that have those disorders, it’s pretty consistent. And because that’s the number one and number two cause of death, we have to look at it and say, “Have large scale genome sequencing projects changed these death rates?” Common complex diseases are fucking complex. There are multiple genes at play with multiple mutations and many, many other things. And there are also lifestyle choices and stress and sleep and a number of other things that are independent of the innateness of these conditions. But if I keep selling you innateness, and keep trying to say we need to sequence more people’s genomes because it will allow us to reduce the widening gap in health disparities when we know that there are better uses of federal money… I don’t know. There’s a hundred different things that we could be using to do that.  That’s not to say that genomic technology hasn’t resulted in some pretty incredible stuff—look at this Moderna vaccine. It’s pretty remarkable, but it’s a targeted thing. It is a piece of drug development. It is innovative, but it has a particular goal.  With respect to common complex disease, I would say that it’s been almost an abject failure. For things like Mendelian disease, though, or identifying the cause of Kabuki syndrome and Miller syndrome and these other really, really rare things, genomic technology has been more successful.


Prompt: The whole “All of Us” project is couched on increasing diversity, because we know there’s a dearth of diversity in genetic data—we know that the people who have been sequenced so far in America do not reflect the full spectrum of human genetic variation. It’s a clever trick though: it uses the illusion of widespread benefits to public health outcomes in order to include minority populations so companies can understand their intimate historical relationships with natural selection, and then use those genetic mutations to fasttrack the development of expensive pharmaceutical drugs that are out of reach of those populations.

Response: And that’s why that paper was in The New England Journal of Medicine: because I was super critical, but I also spoke about the facts, and that’s the only thing they respect. Sometimes you have to be honest. I don’t think that that paper particularly made me a lot of fans at the federal government, but I also think that we need to be critical. Part of science is correcting the mistakes we make and really thinking about what true equity and inclusion means.  “Just-So” Stories There are many popular genetic origin tracing projects that have made a lot of claims around race, biology, and Indigeneity that are pretty problematic. Do these tracing projects have any scientific value? As a genetic scientist, what do you see as the reason for these types of projects?  There’s a lot to unpack there. There’s some complex shit. We actually just created this new exhibit at the Bishop Museum in Hawaii called “(Re)Generations: Challenging Scientific Racism in Hawaii.” A lot of the subtext and inspiration for connecting genetics to race comes from the early days of, like, the Franz Boas, you know, Indiana Jones bullshit, a.k.a. eugenics. We’ve been thinking about phrenology and all these old forms of science.  I think the problem is we consider ourselves totally separate from our past and say, “Those tools of the past, like phrenology and all of these different ways that you measure a human, you know, we’re independent from that now.” Like: “We would never measure the kinkiness of someone’s hair in that way. We would never associate that with race, racism, and social hierarchy.” Meanwhile, what’s going on in La Jolla or the Bay Area is the same exact thing. But it’s really a lot harder to put your finger on those things, because digital data is not something that you can see or taste or touch in that way. So the new form of eugenics and extraction is invisible.  You also have to know that there’s a certain level of certainty and accuracy with the way we return those results, too. Nothing is 100 percent, in the way that the techniques used to survey the genome vary as well. What 23andMe uses or what National Geographic uses is a very low resolution look at the genome. They use a tiny fraction of the actual variation that you have. And then don’t get me started on the way we create categories, because that’s just biased in itself. Come to Hawaii, where we have the highest percentage of people of mixed ancestry in the United States, and show me who’s Hawaiian—good luck. What characteristic are you going to use? Is it skin color? Because that is exactly the same thing that we were criticizing.


Prompt: Could you talk about the connection between genetic determinism and disease likelihood, because one of the things that you mentioned in your papers is “just-so” evolutionary explanations. If you get a high likelihood of a disease on 23andMe, are you just doomed forever? What is a just-so evolutionary explanation? Are you familiar with polygenic risk scores? They’re super interesting. That’s what’s hot right now in our field. They are these algorithms or heuristics that we can use to predict the potential for what we call the pathogenicity of a mutation. We use them to predict whether a mutation is going to be cancerous, or cause heart disease or something like that.  That predictive quality of genomics is something that a lot of people see as the Holy Grail that we’ve been searching for: the difference between predictive medicine and reactive medicine, and that’s definitely where we want to go. Especially with common, complex disease. But when you train every single algorithm on white people’s data, you bias everything, and so none of these polygenic risk scores work in populations that are not white.  Then there’s a second part of it. And this gets to the kind of “just-so” world of it. If you say a mutation is cancerous or pathogenic based on a correlative basis, and you don’t have causative data to show that, that’s problematic, right? You have no proof except the P value, you have no proof except some statistical phenomenon or correlation or racist-ass narrative. And so that has just played out through the field of population genetics forever. There are just all these levels of inaccuracy that get baked into these things and they become real.  One of the most famous examples of this one that I was happy to shoot down was this one that is called the thrifty gene theory. The thrifty gene theory states that Polynesian and Pacific Islander people today have really high rates of obesity and type 2 diabetes because of our history as voyaging people, because if you had these mutations that predispose you to hypercaloric storage on these journeys, it was an advantage. But then once modernity hits, you develop type 2 diabetes and obesity, and it’s because of our genes.

Response: It’s just so racist because it’s like—no, maybe the reason why we have high rates of obesity is because you took away our access to reefs, to fishing, hunting rights and all these other things, and then you replaced our highly nutritious diet of poi and fish with spam, white rice, and soy sauce. Why are you blaming this on innateness and our evolutionary history? You’re also discrediting our accomplishments as probably the greatest seafaring people in human history.


Prompt: Okay, you said you found this thrifty mutation in 2015 in Samoa. This is the most fucked up story of all. You found this mutation in the gene CREBRF, and you say that it’s a thrifty mutation in fucking Nature. Everybody reads Nature. The gold standard. You said that in Nature and yet you have no functional evidence in your paper other than a mouse overexpression, which is essentially punting it? I can tell when things get appended as an experiment on the back end of a paper, like an auxiliary addition. They were asked by a reviewer to do that to show something—they did this bullshit that basically made the data fit a model. Super sketchy, but they got it through. Which speaks to how racist and faulty a lot of these journals are too.

Response: Luckily, I was doing my postdoc in genomic engineering and genome editing. And so I said, okay, what if we use Crispr and take their mutation and then create a stem cell line that includes the Polynesian mutation in one population of cells, and then in the other population of cells we don’t include the mutation? It’s called an “isogenic cell line model”—we’re reverse engineering a mutation from natural selection’s point of view and we’re putting it in the cellular model. Now we can starve it from insulin and see what happens, we can measure the mutation’s effect on a range of things that are involved in producing metabolites and actually speculate on it from the point of view of a causative experiment. That’s the cool shit about genome editing for us. We can actually rewrite evolutionary history and empower Indigenous people by using genome editing. Thank you, Jennifer Doudna.


Prompt: But, again, it’s about who uses the tools, right? And, lo and behold, what we’re seeing is very different from that paper. So we’re seeing that that mutation is at a much higher percentage of people that are of Polynesian ancestry that are professional athletes, seeing that it’s associated with bone density, muscle density, and other things—we don’t quite know yet fully, but it’s definitely not a fucking thrifty gene, tell you that much.

Response: Now the other thing that they did—Steve McGarvey from Brown University—is they tried to file an international patent claim around the mutation and they didn’t include anyone of Polynesian ancestry in the patent claim. And they put it up there. And I think because I went to like every single genetics society and medical society and just buried them since 2018, it didn’t work. Not just me, but others that are from the culture, to my Maori colleagues, my Tahitian colleagues, my Samoan colleagues, my Chamorro, my Taiwanese colleagues, everybody was like: fuck this and fuck you, you have no right to try and commodify this, you know?  That was the moment where I started thinking intensely about IP, commodification and natural selection. It was so close to home and was this weird moment where I was like, “Oh shit. They are going to just keep doing this.” Indigenous Futures In the context of patenting and data sovereignty, could you talk about what you’re doing at the Indigenous Futures Institute at UCSD?  We have begun to think about data as a resource, and we’ve begun to really recognize data as the number one commodity on planet Earth, much like everyone else. And it’s not just us. It’s not a moment, it’s a movement, and so there are tons of different people that are thinking about this in different spaces.  My hope is that people like Deb Haaland, the new Secretary of the Interior who is Laguna Pueblo, will represent our interests. And I’m hoping that as we have these conversations about land and what the Department of the Interior is responsible for, they’ll really begin to think about data as a resource and being responsible for data. Because what that does is it allows Indigenous people to be in control and have governance over our data. And once that happens, that can then be used to buy back land and begin all of these new forms of stewardship and guardianship of land and revitalization of our cultures. So that’s a very holistic way of thinking about Indigenous data sovereignty.


Prompt: What we’re doing at the Indigenous Futures Institute is we’re really thinking about ways to do that. This work is couched within our design lab and engineering school. We’re intentionally trying to make it as multidisciplinary as possible, but sometimes it’s hard to be disruptive and chaotic in academia. But we’re trying our best, you know. We have a range of different focuses, but it’s about codesigning technology with communities and being iterative about that process.  That could range from the story I just told you about what happens when Indigenous people use genome editing to think about our past, or what happens when Indigenous people use machine learning and deep learning to shine a light on the past. It could include automating our archival records. It could also be thinking about creating deterrent and safeguarding technologies to avoid museum collections from sequencing our ancient genomes without our consent. There could be a number of different ways that we use those technologies.

Response: Then there’s the focus on education, educational tools and creating a safe space for all of our Indigenous and historically marginalized communities of people to come, learn, and be productive and innovative. We have a huge focus on sustainability, whether it’s environmental sustainability or looking towards Indigenous innovation. Within architecture and urban planning, what does it mean when Indigenous communities are in the driver’s seats of those fields? And how do we actually begin to look towards knowledge that is quite ancient for modern solutions?  So that’s it in a nutshell. Some of the people that are involved there are Provost Wayne Yang and Theresa Ambo, Sarah Aarons, Manuel Carrillo. That’s the core group, but it’s very new. We’re also thinking about Indigenous takes on economics. Like, why is your financial projection quarterly? Why isn’t it, you know, ten generations? Does decentralization guarantee equal distribution of power? Who do decentralization and distributed technologies currently benefit, and who should they benefit? The distributed web has become a common buzzword covering everything from the blockchain to peer-to-peer communication protocols and file storage. Decentralized web projects purport to solve the inherent problems with the internet as we know it today, promising to democratize internet governance through distributed ownership and control over web infrastructure.


Prompt: We sat down with Mai Ishikawa Sutton, lead organizer of DWeb Projects with the Internet Archive and cofounder and editor of COMPOST, an online zine about the digital commons, to discuss what the distributed web and DWeb are, community principles as an organizing tool, and the ways decentralization is a verb not a noun.

Response: Could you tell us about your background and political and technical evolution? I went to UC Santa Cruz for college and was part of a program now called the Everett Program. The program focuses on training undergraduates on practical technologies—like contact databases and website building, branding, social media, things like that—and pairs them with nonprofits who have concrete technical needs. It’s a student-led, student-taught program with the goal of helping students become what we call “social justice tech entrepreneurs.” As part of the program, I went to Malaysia and worked on the technology side of a Muslim feminist organization.  After graduation, I didn’t know what the hell to do with my life, so I dove into readings about internet policy and got involved with net neutrality activism. That brought me to the Electronic Frontier Foundation. My initial role there was to support all aspects of their international advocacy work around free expression, privacy, and intellectual property. I eventually chose to work on activism against international copyright laws. At the international policy level, copyright policies are largely decided through trade agreements—Hollywood and big publishers can essentially have their copyright wishlists enacted into the national laws of countries that sign on to these opaque trade deals. Copyright raises a lot of interesting issues and questions around creativity online: How can we make sure artists are paid for their labor, and how does that determine how people engage with culture online?  At some point, I wanted to figure out how to be on the other side of this equation. Instead of arguing against the endless terrible corporate policies of multinationals, I wanted to fight for positive initiatives. I knew there had to be an alternative, a whole other approach to economic policy. So I left EFF and went to an organization doing solidarity economy advocacy called Shareable. Their advocacy covered the commons as it relates to stewarding everything from land, water, waste, and technology. My work there allowed me to explore this alternative economy: What is the commons? What is a cooperative? What is the essence of these things that people own, share, and steward together?


Prompt: I always had an interest in applying what I learned to the realm of technology. The platform cooperativism movement was emerging at the same time, and the first DWeb Summit happened in 2016. I attended that summit and came away with this feeling that there was an opening happening: this was a blossoming community of people who wanted to question the ownership of technologies, to question how infrastructure was being built and who was controlling it. And they weren’t just talking about it, they were actively building alternatives.

Response: So after Shareable I worked on projects that took me in that direction of supporting and building alternatives. I worked at the Oakland Public Library, which completely changed my view on all this. I now focus on projects where I feel like there’s potential to experiment and shape how people think about decentralized technologies—but more than that, build networks of solidarity based on organizational decentralization and interdependence.  Decentralization and distributed systems have had a long history on the internet, but recently it feels like there’s been a focused energy and community forming around particular decentralized technologies. What’s your perspective on the historical context that informs these current movements? Decentralization is a core tenet of the internet and the development of the web, but it’s had its peaks and valleys. Email, which was invented in 1971, is the most solid decentralized protocol that has enabled interoperability in a way that many other protocols haven’t.  There was a long valley of consolidation after that. But then decentralization hit a new renaissance with the filesharing era. As a millennial, the trend that’s most memorable to me was the peer-to-peer networks in the late ’90s and early 2000s like Pirate Bay and Napster. These emerged in response to the way the internet provided great technical affordance to access and remix content, while legal apparatuses like the DMCA (Digital Millennium Copyright Act) got in the way of doing that. Laws like the DMCA led to a backlash against big entrenched content publishers like Hollywood—an industry that had not properly contended with the ways the internet undermined their copyright monopolies. There was a big boom in filesharing, using new decentralized tech like torrents, and this helped people to change their thinking around the structure and purpose of the internet.


Prompt: But then for various reasons—one of them being that the original P2P protocol developers didn’t embed privacy into their systems—a lot of people ended up being vulnerable to the legal consequences of using these technologies. This prevented the full potential of P2P technologies from being realized back then.  Fast-forward: since the teens there’s been a widespread trend towards the centralization and monopolization of platforms and social media. I think this new wave of decentralization is in response to that. We can clearly see how mega-powerful companies are trying to define what the internet is for their own benefit and control every aspect of our social interaction. And so people are trying to imagine: what are the alternatives to that?  The rise of the blockchain created a new way of distributing information and infrastructure. But a less talked-about, parallel trend is the rise of community networks. Namely in Spain, Slovenia, and in parts of the United States, there are networks where the last-mile internet infrastructure, from the service provider to the home or business, is owned by the community. This movement highlights how community ownership improves access, and prevents things like data throttling, data caps, and other discriminatory behavior that undermines net neutrality. Community networks demonstrate how internet infrastructure can be collectively owned and controlled in a decentralized manner. They’re an alternative to corporate, top-down, profit-driven services that undermine our privacy and expression online.

Response: Distributed Community Building In San Francisco, these ideas around decentralization and modern distributed protocols can sometimes be mediated through a particular conversation around this idea of “the DWeb,” which is informed by institutions such as the Internet Archive. How do you think about labels like “DWeb” or “IndieWeb,” and the efforts to build communities around them? I went to the DWeb summit in 2016 and, I have to say, I was not impressed. It felt like an endless barrage of startup pitches by people who looked all the same, standing up on the stage and describing how they’re going to decentralize infrastructure, and how it’s going to save the world, upend this market, and change everything. To me, if decentralization has any political meaning, the people building it have to be very different from the types of people who built the World Wide Web. We can’t just replace the platforms and protocols we have today with other purely profit-driven companies that can only call their product “decentralized” because the technology functions in a more distributed manner. For decentralization to be a remotely revolutionary concept, we need to question the internal logic of the tech industry itself—how people are incentivized to build things, how people are treated in the process, and how the relationships and systems we operate with are controlled. How does decentralization redistribute power? That’s the fundamental question.  There are currently loosely three factions around decentralization. The first one is this idea of “DWeb,” which is probably the most politically agnostic group. The DWeb movement is a pretty big tent—accepting that there are all kinds of interesting decentralization projects with different approaches that can learn from each other.  Then there are the people who want to call it a “distributed web” or the “peer-to-peer web”—there’s a strong emphasis in that community on “distribution” as a way of looking at the governance and ownership of the network.


Prompt: And then the third faction is “Web 3.0,” which is the crypto and blockchain community. They see themselves as part of the evolution of the web and how it is monetized. They see the legacy of the web being extended through these crypto-based decentralization technologies and see their tools as the new engine of the tech economy.

Response: The Internet Archive’s work has played an interesting role as a mediator and convener of these different ideas. We so far haven’t explicitly excluded certain approaches, though the projects we have highlighted fall more in the peer-to-peer DWeb side of things, rather than Web 3.0. We have curated a dialogue around these trends and elevated open source projects that are more community-driven and are more explicitly focused around having user governance and control.  As part of my role facilitating these conversations, I came to work on the creation of the “DWeb Principles”—to define what the community finds important and wants to identify as its goals, not just in terms of the centralized forces that they are in opposition to.


Prompt: That seems like it gives you the opportunity to curate the conversation around “what we are talking about when we talk about the distributed web.” The DWeb Principles came out of that vacuum of political agnosticism, to identify what it is that we are actually saying when we say we want to “decentralize” the web. We were very explicit that it’s not just another new set of principles that ignore or replace other ones. We cite many other principles, like Association for Progressive Communications’ Feminist Principles of the Internet and the Design Justice Principles. Our principles emphasize that this particular community is concerned with design issues like interoperability, being free and open source, having repairable devices—we tried to really name the things that this community cared about. And having put that first stake in the ground, I hope that will shape the dialogue around what it is that we actually mean when we talk about decentralization and how some of these technologies are being built.  Declaring those principles feels like an opportunity to move the center of the conversation for the people already involved, and supply a different set of baseline assumptions for people newly being introduced to the concept of the decentralized web.  Yeah, exactly. The act of creating these principles is also in and of itself a tool for people within the space to have a dialogue with each other. It’s similar to the kind of inter-organizational statements I used to coordinate as a digital policy organizer. The process of creating them involves a ton of back and forth, and it’s this back and forth that’s so valuable. Creating an environment where people can be in dialogue with each other to assert their values, listen to others, and negotiate the desires of their shared dreams—that’s a powerful tool of organizing! The hundreds of edits across the draft versions are artifacts of this process. One of our goals with the principles was to bring the community of people interested in decentralized technologies a bit closer together. Our other goal was to have a document we can use to introduce people to this space and succinctly describe what we’re about.

Response: Putting the principles into practice requires recognizing our own needs and addressing them in creative ways in solidarity with others. That is what centralized top-down systems cannot do—create locally-situated networks of communication that emerge out of the imagination of people who live in their unique economic, social, cultural contexts. If decentralized technologies are designed with an assumed universality—that there can be one blueprint for organizing communication across diverse knowledge systems and language cultures—they’ll become just as problematic as Facebook.  The act of creating the principles is useful in and of itself, as an act of community organizing beyond whatever the output is. Even if the output was to disappear, the connections between the people and the organizations will still exist.  Totally! Investors and people in Silicon Valley go to parties together where all this soft power, informal trust-building happens. And it sucks to some degree, but we’re humans and that ability to talk to each other and establish personal trust is really, really important. You need to have new ways of creating alternatives to high-end galas and backchannel investor conversations, and have other ways for people who are trying to build alternatives to talk to each other and build trust.  What Works in Bangalore A lot of the projects that get airtime in conversations around decentralized and distributed technologies seem to be centered in the Global North. What are some ways in which your activism has connected these new kinds of technologies to communities in the Global South?  Before DWeb Camp in 2019, we connected with the Association for Progressive Communications (APC), an amazing organization that is essentially a coalition group and advocacy organization that connects progressive and feminist tech-based organizations from around the world. [Ed: DWeb Camp was a four-day retreat in Pescadero, CA, organized in association with the Internet Archive.] Through that connection, we invited ten global fellows from APC’s Community Networks program to the camp. They came from India, South Africa, Brazil, and elsewhere, to facilitate knowledge exchanges about internet infrastructure, open hardware, and community networks.