Prompt: In this issue we see the sins of the father continuing to be visited, if not on the son, then on other people’s children. Our authors explore how new systems replicate old inequalities. In South Africa, automated credit rating systems built on decades of racist data re-segregate cities; despite formal equality, algorithmic apartheid succeeds apartheid. In the United States, Black families are subjected to obscene levels of surveillance and violence, not only from the state but from private agencies to which the state has delegated its coercive power. Under these circumstances, our author recounts, having a neat house when the family police show up in the middle of the night can be the difference between keeping your children and having your family destroyed.

Response: Here, and elsewhere, we also glimpse homes as sites of creativity and resilience. Right now, TikTok influencers are making art, or at least satire, out of homes that Zillow makes into content, to entertain members of a generation that is struggling to afford housing. Garment workers moving between villages in China’s Zhejiang Province and Prato, Italy are finding opportunity and resilience in family networks, if also exploitation. So, presumably, will the migrants who succeed them make new kin.  Google Nest and its imitators would have us believe that we can remain kings of our castle, and everyone in it, even when we are away. By contrast, we share the view of the philosopher who said that it is part of morality not to be at home in one’s home. You can take us out of our home, but not our home out of us, and for those who are lucky, home may be a site of nostalgia, even if you can never go home again.


Prompt: Baltimore City Public Schools is on a spending spree. The district’s budget is projected to increase $227 million in 2023—an increase of nearly a sixth of its current budget, despite falling enrollment. Greater state funding from the Blueprint for Maryland’s Future Act accounts for much of this increase, but a significant amount comes from federal sources. These federal funds are part of the roughly $190 billion that has been earmarked for education in 2021’s big spending bills: the CARES Act, the Consolidated Appropriations Act, and the American Rescue Plan Act.  So, in the fall of the 2021–2022 school year, my district asked for help in determining where to allocate some of this newfound wealth. High school staff and students were asked to vote between two fully digitized English curricula that the district might purchase. In my ninth grade English class, I projected the flashy promotional videos made by each company, as my students filled out a response survey on their laptops. Both presentations promised that students would be empowered. (If you wish to hear this word every day until you die, become a public school teacher.) Both presentations tried their best to make the everyday activities of an English curriculum—writing essays, preparing for debates, and answering questions on articles and novels—sound exciting.  One of the companies, Imagine Learning, distinguished itself by claiming that, on their platform, students could demonstrate their knowledge of English language and literature by “composing a rap song or creating a TikTok.” Some students seemed intrigued. Others groaned over their laptops. Yet another edtech firm was promising to uplift marginalized students by filling the classroom with the kind of entertainment media they consumed at home.  Such products can make educational content more accessible, especially at home, but they cannot actually address academic shortcomings any more than pen and paper. This is because learning happens through what educators call “productive struggle,” not merely the consumption of educational content. Productive struggle is the profession’s term for problem-solving at a level that is difficult for a student, but possible with effort and limited assistance. Educators refer to this magical window of learning as the “zone of proximal development.” Any education technology that is able to employ entertainment to transcend the difficulty inherent in learning ceases to be educational.

Response: But even if such a technology is able to elicit productive struggle among students, it still runs up against a deeper challenge: poverty. The real reason that students have difficulty in the classroom is not due to the lack of thoughtful UX design, but because the trauma and instability wrought by the material circumstances of their home and community make engaging in productive struggle difficult. This does not mean that edtech products are useless. But they are not able to address the core cause of educational inequality any more than traditional instruction.  The recent growth in school funding from Covid relief measures gives educators a long-overdue opportunity to confront the material roots of educational inequality. But this influx of public money also represents a potential payday for a fast-growing edtech sector. The edtech market in the US is expected to grow to about $60 billion by 2026, according to an estimate from Global Industry Analysts, more than doubling its 2021 valuation and drastically outpacing the growth of the education sector as a whole. At a moment when governments are committing real resources to public education, companies touting disruptive digital approaches see a gold rush. They plan to win lucrative contracts by promising to solve a problem they can’t possibly solve.  Passed, Present, Failure  When asked about how to address the legacy of slavery in an early Democratic primary debate in 2019, Joe Biden wound his way through a surreal answer that ended with him advising parents to “make sure you have the record player on at night, the phone, make sure the kids hear words.” While many viewers were probably confused by the now-president’s statements, those of us in education knew exactly what he was referring to. He was regurgitating the long-held belief that poor children suffer academically due to the “word gap”—a disparity in the complexity and the volume of words heard by children in rich and poor homes.  Biden’s statement is the perfect encapsulation of a particular tradition in American education. This tradition, rather than trying to address the historic inequities that drive educational disparities, relies on something that is already in nearly every American home regardless of income: entertainment media. From phonographs to television to the internet, the wide availability of entertainment media has led generations of educators to embrace it as a potential salve to problems in American education, despite little evidence of its effectiveness.


Prompt: Early in the twentieth century, the Victor Talking Machine Company led a major campaign to market its “talking machine” phonographs as educational tools. Victor was the first edtech company, touting a wide variety of potential educational benefits from its technology, from learning roller-skating and typewriting to French. Despite these claims, clear-eyed educators quickly recognized the limits of the machine. Writing in 1918, at the height of Victor’s educational marketing campaign, Charles C. Clark, a professor of foreign languages at Yale, argued that the machine “taught only when its master makes it teach.” Citing an example of a friend who failed to learn Italian from recordings that he played every day while eating lunch, Clark noted that only those who would already do well learning through coursebooks could learn with the phonograph, as serious study was needed regardless. Writing six years earlier, a French language professor, Charlotte J. Cipriani, observed that the phonograph could help with French phonetics but could not “relieve teachers and students from the distasteful task of thinking”—in other words, productive struggle.  By the latter half of the twentieth century, the record player had ceded its cultural dominance to television, which brought its own wave of advocates and critics arguing over its place in education. Television became the dominant entertainment medium precisely because, to borrow Cipriani’s words, it relieved viewers of the “distasteful task of thinking.” And, as with the phonograph, educators embraced television not because of its usefulness in pedagogy, but because of its ubiquity in students’ homes. In 1979 an editor for a prominent education magazine, Kathryn LeGrand-Brodsky, argued that “realists” must use television in education because of the “simple fact that children do watch television, and a lot of it.” This is the foundational rationale for using entertainment technologies in education: it’s there, so we might as well use it.

Response: Achieve 3000 With the mainstreaming of the internet in the early twenty-first century, a new edtech product emerged: the massive open online course, or MOOC. Unlike the phonograph or television, which were entertainment media that could just barely be packaged as educational, the internet allowed for rigorous educational content to enter homes with the same ubiquity as entertainment media. MOOCs promised to break down the walls between home and Harvard, liberating education from its ivory tower. In the early 2010s, optimistic writers and educators heralded the arrival of online courses—often free and taught by professors from elite universities—as a great leveling event. In a 2013 op-ed, Thomas Friedman claimed that “nothing has more potential to lift more people out of poverty.”  Unlike the phonograph and the television, MOOCs can be rigorous: they have the ability to foster the conditions for productive struggle. Moreover, unlike traditional universities, MOOCs can allow everyone, regardless of income or location, to learn. Still, MOOCs fail to provide the social support that students need to be successful. This explains their very high rates of incompletion: according to a 2019 MIT study, a large percentage of MOOC students fail to complete their courses, including for degree programs. And the broader accessibility of MOOCs has not led to any real equity in education, with participants from very developed countries passing their courses at 48 times the rate of those from the least developed countries.  The rise and fall of MOOCs makes clear that support, not access, is the key to achieving equitable educational achievement. These realities have led many MOOC platforms to pivot to working with traditional institutions to help award expensive online professional degrees. These partnerships have greatly benefited the bottom line of MOOC providers, with Coursera, the world’s largest MOOC platform, valued at over $7 billion dollars. Such figures dwarf the valuations of the major education media companies of the television era, with Disney buying Baby Einstein for only $25 million in 2001, and Hooked on Phonics reaching a peak valuation of about $100 million in the 1990s. With such money on the line, a slew of edtech companies have entered the market in the past decade, often funded largely by public school districts.


Prompt: Among the products at the forefront of the new edtech wave is Achieve 3000. Achieve is one of the programs licensed under Baltimore City School’s Math and Literacy Intervention Programs budget, which shot up from $10 million in March 2020 to a staggering $28 million in November 2021, thanks in large part to the CARES Act. The program features thousands of readings and exercises, presented much like the Netflix homepage, with the newest articles listed across the top of the page in rows of suggested topics with names like “Teen Channel” and “US History.” Its main innovation is that the same articles are written at different levels, so that students will each be given a text on the same topic that is targeted to their developmental stage. As they answer questions correctly, the difficulty of the readings will gradually increase.  For students who are able to focus on the readings, and who have the language skills to engage with the readings, the program works well. The way that the software continuously calibrates the difficulty level for each student helps create the conditions for productive struggle. But for those students who are already falling behind, the program does little to help them advance.

Response: During the pandemic days of distance learning, if one of my struggling students turned on the camera or microphone, there would almost always be a hurricane of activity and noise going on behind them. If any of my students who were excelling did the same, they were almost always in a quiet, empty room. The few students I taught who were fortunate enough to have their own room, no siblings or children to babysit, and a parent with a job that allowed them to monitor their child’s progress easily at home, excelled on the Achieve platform. Students who did not have such privileges, whose parents worked “essential” jobs, fared about as well as the millions of MOOC students who churned out of their courses. Programs like Achieve work, all things being equal—but things are not equal, and no amount of spending on edtech products can level the playing field.  Making Students Whole Baltimore City Schools and many districts like it around the country are now flush with Covid relief cash. This means they have plenty of money to spend on edtech products. So much money, in fact, that Baltimore decided to license not one but both digitized English curricula that I presented to my students, at an annual cost of over $1.75 million. In fact, the $28 million “intervention technology” budget, which only covers a portion of the edtech products licensed by Baltimore, is greater than the district’s entire “student wholeness” budget for 2022, which provides funds for advising, mental health services, and social-emotional learning. Concerningly, edtech companies are even beginning to encroach into this territory, offering digitized versions of services that could only ever be effectively provided through a human connection.  With only a $750,000, three-year contract, Colorado-based Pairin is a relatively small player in Baltimore’s edtech procurements. However, its contract is a particularly insidious example of an edtech company receiving funds earmarked for social services. For Baltimore City Schools, Pairin made a new product called Plan2BMore, which is meant to serve as a sort of virtual guidance counselor and claims to be customized for the needs of Baltimore schoolchildren.


Prompt: Unfortunately, it does not work.  When my school attempted to get students to use the program in March 2022, it constantly crashed. This was an issue district-wide. More importantly, Pairin represents the attempted digitization of the exact needs that Baltimore should be addressing through hiring counselors, advisors, and social workers, or by providing other services administered under the district’s meager student wholeness budget. Giving guidance to students and helping them set a path for their future is a deeply personal act, and one where a human connection can help immensely. It is also an area where students in Baltimore City Schools, who overwhelmingly do not have parents who have graduated from college and who live in impoverished communities, need far more support than their wealthier peers in the suburbs. Such guidance, along with myriad other social services, could help motivate students to engage in real productive struggle, justifying the investment in products like Achieve 3000. To contract out such work to a faraway for-profit firm sets a worrying precedent for how Baltimore City Schools will allocate its rapidly expanding budget going forward.  Good edtech products have a role to play. But the content that we stream into classrooms or homes is simply not as important as the material inequities faced by students, and a school district’s ability to relieve those inequities through counseling, guidance, and other social services. If school districts and the governments that fund them want real change, they must address the material needs of students at home and in school. Otherwise, they risk becoming yet another pipeline for the transfer of public resources to the private sector.

Response: At first glance, the bedroom may look like the exact opposite of a political space. If we follow Hannah Arendt’s definition of politics, informed by Aristotle, as comprising any action that is performed in public, bedrooms are quintessentially apolitical. They are where we sleep, make love, tend to the needs of our children, recover when sick, and seek refuge from the outside world.  Yet in our era, this most private of places has been absorbed into the political realm: as a site of our online interactions, as the stage of our self-presentation, and even as a theme of many recent activist campaigns. Exploring this emerging “bedroom politics” stands to reveal something important about the shifting relationship between private and public, and how our political life is being transformed as a result.


Prompt: These changes are affecting the content of contemporary politics. The transmogrification of the bedroom from a place of rest into a space in which people not only sleep but live and work—and, crucially, participate in political conversations online—goes a long way toward explaining both the form and content of contemporary politics. This turn brings both opportunities and risks. On the one hand, bedroom politics may offer an opportunity to re-embed causes that may seem lofty in everyday life. Making the political viscerally personal may help politicize more people. On the other hand, the convergence of the personal and the political may create a situation where we are no longer able to distinguish between the two. Regardless, there is no way to understand the logic of contemporary politics without thinking about the bedroom.

Response: The Conquest of Bed Traditionally, the places of politics have been squares, streets, conference halls, churches, meeting houses, and party offices. In his influential analysis of the bourgeois public sphere, Jürgen Habermas observes that such sites are the physical equivalents of the news media: they are where ideas circulate. The philosophers Oskar Negt and Alexander Kluge argue for the existence of a proletarian public sphere, which coheres around humbler locations like factories, pubs, and working-class neighborhoods.


Prompt: Whether bourgeois or proletarian, however, the public sphere historically excluded the home. This exclusion goes back to Aristotle and other classical philosophers, who believed that the bedroom in particular was the deepest and most protected recess of the oikos (household), far removed from the radical openness of the agora, where the democratic life of ancient Greece took place. Yet even in antiquity, some of the most important political conversations, especially within influential families, were conducted in the household. Further, the home was the only place where (aristocratic) women, otherwise barred from the public sphere, could participate, if indirectly, in political affairs. They could give advice, participate in private discussions, or, in the modern era, act as the patronesses of salons. The home has also played a central role in underground political organizing throughout history: as a venue for secret meetings, where one can escape police surveillance.

Response: Still, these intrusions of politics into the household remained exceptions to the rule. It is only with the development of modern media—from the printing press in the fifteenth century to the first modern newspapers in the eighteenth century, to electronic media such as radio and television in the twentieth century—that the home became progressively incorporated into the public sphere, and the line between the private and the public began to break down. The first part of the home to be annexed was the kitchen, where the daily newspaper would be read at breakfast time, in what Hegel famously described as the “realist’s morning prayer.” Then came the living room, where, beginning in the 1950s, families would gather around the TV to watch the evening news, in what became a national ritual in many countries. But the bedroom remained intact as a realm of intimacy—a place where people could take refuge from the outside world.


Prompt: Yet with the digital era, even this bastion of privateness seems to have fallen. The popularization of the internet and social media, and the fact that they are often accessed through portable devices like smartphones and laptops, has unanchored media consumption from specific sites. The bedroom is now as permeated by the public sphere as anywhere else, perhaps even more so—it has become, in the words of the scholar Zizi Papacharissi, “the mobile and connected enclosure of [our] private cocoon.” Indeed, research has shown how bedrooms have increasingly become one of the main sites of digital media use. A 2017 study published in the journal of the American Academy of Pediatrics showed that 75 percent of children and 70 percent of adults report viewing or interacting with screens in the bedroom. This situation has led to couples complaining about the fact that smartphone use negatively impacts their sex lives, as well as growing concern that “doom-scrolling” in bed negatively impacts our sleep patterns.  The use of digital media in the bedroom is a reflection of the portability of such media in the era of the smartphone. But it is also a reflection of worrying socioeconomic trends. Rising housing costs and the difficulty to get on the housing ladder means that a growing number of young adults are now living with their parents. According to a 2020 Pew report, 52 percent of US citizens ages 18–29 lived with their parents, up from 47 percent before the pandemic. In the UK, according to the Office for National Statistics, around 42 percent of 15–34 year-olds did the same, compared to 35 percent in 1998. Further, the number of people flat-sharing with friends and strangers has grown, with some reports of a fourfold increase in the 2010s, particularly pronounced among those ages 45–54. This means that, especially in big cities, bedrooms have become mini-apartments: multifunction places where people do not just sleep, rest, and “disconnect,” but where they work, study, and use the internet. For teenagers, meanwhile, bedrooms are the only place in the household where they can engage in online conversations without their parents meddling.

Response: Movement Homes The places where politics take place have important consequences for both its form and its content. So what are the implications of the bedroom becoming a place of politics? At the level of form, we see bedrooms everywhere in online life. They have become the contemporary equivalent of the speaker’s podium. Bedrooms appear as the backdrop in political TikTok and YouTube videos, as well as in political meetings and talks conducted over Zoom. A widely read 2019 report on the climate movement carried a picture of Paul Campion, a Sunrise Movement activist, in his bedroom in a small apartment in Washington, DC with the caption “the organization’s ‘movement home.’” At the level of content, politics from the bedroom has to some extent become a politics about the bedroom. Issues that are associated with the bedroom—from sickness to sexuality, housing, and mental health—have become more prominent in political debates. In the era of social media, it has become easier to make the issues contained in the bedroom visible, by representing them though pictures, videos, and other multimedia material recorded there.  Bedrooms and beds have appeared in many recent campaigns. They appear in the campaigns of feminist and LGTBQI+ groups as a means to discuss issues of sexual and reproductive rights, as in #MeToo activist Alyssa Milano’s call for a sex strike, a form of protest that by its very nature is located in the bedroom. Similarly, housing campaigns such as Generation Rent and #VentYourRent launched in London in 2016, as well as the Deutsche Wohnen & Co. enteignen referendum in Berlin in 2021, often featured images of beds and bedrooms, and saw people complaining about unhealthy housing conditions and difficulty sleeping. Some scholars have even coined the expression “bed activism” to describe the various campaigns where bedrooms and the act of sleeping are a key theme.


Prompt: This politicization of beds and bedrooms is not altogether new. Famously, John Lennon and Yoko Ono staged a “bed-in,” spending days on end in their bed to protest the Vietnam War. But at the time, their protest was widely criticized as illegible: it was not clear why they would do it in the bedroom. These days, the bedroom is clearly a space where politics takes place.

Response: Beyond the Bedroom What are the broader democratic implications of bedroom politics? What possible opportunities are opened by it and what potential risks may it involve? Bedroom politics are an attempt to re-embed politics in everyday life. They present an opportunity to politicize issues having to do with sickness, sexuality, housing, and vulnerability that deserve to be part of our political conversations. In our hypermediated world, bedroom politics not only highlight that “the personal is political” as feminist and queer movements have already argued, but also that the political has become deeply personal. Further, bedroom politics gives us a new way to do politics together. In her essay “Sick Woman Theory,” the writer and artist Johanna Hedva claims the bed and the internet as parallel sites of resistance. She pushes back on traditional take-to-the-streets constructions of political activism as able-bodied: “If being present in public is what is required to be political, then whole swathes of the population can be deemed a-political—simply because they are not physically able to get their bodies into the street.”  However, bedroom politics can also function as a catalyst of individualized politics. A politics of the individual asks what the individual, as opposed to the community, should do. People sharing stories and demands from their bedroom may be inclined to act in ways that are solitary rather than solidaristic. Bedroom politics can thus also be read as an example of the broader turn toward narcissism that has been denounced ever since Christopher Lasch’s famous The Culture of Narcissism came out in 1979: a politics that places personal experience above all else.


Prompt: Lyn started babysitting when she was twelve. And yet, despite her nearly twenty years of experience, she recently found herself unsettled while at a nannying job: she was being recorded on camera. Playful activities like building blanket forts, she told us in an interview, became laced with anxiety. “I intentionally think about the angle of the camera,” she said. “I’ll make really bad ones, with a lot of space and holes, so that the camera can still see us.” She devoted time to “making sure my mannerisms and my posture and, like, the way that I’m speaking to these children that, if it’s recorded on the camera, that there’s nothing that would look bad.” Lyn found her job on Care.com, a platform for connecting nannies and other care workers with new clients. Founded in 2006, it now boasts a user base of thirty-one million across twenty countries. Its sweeping size and reach grants the platform an incredible amount of power to shape domestic work arrangements. It not only attracts individuals seeking care support, but also corporations and academic institutions, which provide free and subsidized subscriptions to Care’s Premium tier to their employees through the platform’s Care@Work program.  In its own words, Care.com is “the world’s largest online destination for care. We connect families with caregivers and caring companies to help you be there for the ones you love.” This emotionally laden statement is immediately followed by a coldly formal liability clause: “Care.com does not employ any care provider or care seeker nor is it responsible for the conduct of any care provider or care seeker.” As part of this liability-avoidance strategy, Care encourages its users to surveil the care workers they hire, suggesting on a “Safety” page that clients should “continually monitor your caregiver’s activities.” It makes sense, then, that in our extensive interviews with a diverse cohort of nannies from across the United States who use Care.com (and similar platforms like SitterCity), in-home surveillance was a recurring topic. We found that Care’s business model as an employment platform and subscription service not only explicitly and implicitly encouraged “monitoring”—which often reached uncomfortable levels for the care workers subject to it—but also made it difficult for nannies and babysitters to effectively advocate for themselves or negotiate employment conditions.

Response: When nannies moved off the platform and into the space of a client’s home, they were often monitored through a variety of surveillance technologies and techniques—not just cameras, but also text messages, paper forms, and even the children being cared for. Cassandra told us that one of her clients expected “constant updates and documentation,” including photographs of the plates of food the children were eating. (All names have been changed to protect the privacy of our interviewees.) It made her feel as though her clients didn’t trust her to feed the children properly—worse, “It also made my job a lot harder, because like, when I’m texting, I’m not good at multitasking,” she said. “So, like, the girls would be trying to talk to me and I’d be like, ‘Hold on. I’m trying to take a picture of your food.’” Care.com’s marketing materials are geared towards parents, not workers. Until recently its website proclaimed, “Your safety is our priority: We are committed to helping you find a caregiver you can trust.” The platform preys on the feelings of protective parents—one of their blog posts intones that “nothing is more important than keeping your children safe from harm”—and pushes two contradictory messages. First, Care’s platform encourages parents to buy more stringent background checks, accomplished through direct contact with the carceral state, ranging in exhaustiveness from a database search of court records at one end, to a “hands-on” investigation performed by a team of “highly trained researchers and licensed private investigators” at the other. At the same time, Care’s messaging to parents implies that background checks are not enough to keep their children safe.  Regardless, nannies told us that monitoring makes them worse at their jobs, not better: it adds to the daily workload, and often prevents them from doing their job without interruption or from taking a break without feeling self-conscious. Indeed, the only party that clearly benefits from constant parental surveillance is Care, which tells families they need to monitor their nannies—painting them as suspicious by default—both discharging its own liability, and profiting from the background checks and membership fees that promise increased security.


Prompt: Ongoing monitoring The Care.com platform is split into multiple apps. On “Care.com Caregiver,” nannies, dog walkers, cleaning workers, and others can sign up, submit to background checks, fill out their profile and upload photos, and search and apply for jobs. Another app, titled simply “Care.com,” allows care seekers to create job postings and browse caregivers’ profiles. With a Premium subscription (listed at $39.99 USD monthly, $89.97 quarterly, or around $160 USD annually), users can also contact caregivers directly—a power that was abused to repeatedly send unwanted messages to one of our interviewees—and to “have access to results of all background checks,” along with other “benefits.” Workers can also sign up for Premium for the same prices, granting them access to “higher ranking in search results,” and a promise that they will be “5X more likely to get hired.” Care Premium also comes with a free annual “CareCheck” background screening, which is required to begin applying for jobs. If workers choose not to sign up for Premium, the CareCheck costs $14.99 USD per year, due when they register.  Care.com’s official materials admit that background checks can be inaccurate, yet two of our interviewees discovered that the platform’s bans are decisive and uncontestable, and the platform doesn’t disclose whatever supposed violation led to the ban. In addition, the platform advises care seekers to purchase additional background checks on their applicants “at time of hire” for increased security. They note that background checks are “not a substitute for conducting thorough in-person interviews, reference checks, online and social media searches, obtaining copies of the candidate’s identification documents, and conducting ongoing monitoring of any individual you hire.”  “Ongoing monitoring,” however, is not simply used to ensure the safety and well-being of one’s children. It’s also a retaliation tactic. Kendra told us that her client ramped up monitoring immediately after she attempted to renegotiate her pay and hours. “She had started asking more of me after that. So it was like, ‘Oh, well, you’re gonna need to step up your game.’ And it was like, ‘You need to write down every single thing that my kid does’… It was almost like a punishment… I had to write down every activity, I had to write down everything they ate, every time they went to the bathroom, every time. Like, everything.” This documentation took place on printed sheets of paper that her client supplied to her, which were inspected before she left the home “to make sure I did a good job.” She described this work as “exhausting, especially on top of having two children to watch. I would have to take time away from them to fill it out.”

Response: Nannies sometimes don’t know the full scope of monitoring until they start a particular job—and the fact that their jobs are usually performed in clients’ homes puts them at a disadvantage in negotiations. As Marie put it: “People can act whatever way they want to on the phone,” she said. “Until you go into their house, you just don’t know.” She described one interview that stuck with her: “We’re just gonna watch you,” a parent told her. She was instructed to take their baby, a bottle, and a three-year-old downstairs while the parents observed her on a camera feed.  “There is no privacy” Marie felt deeply uncomfortable that the family hadn’t told her before the interview that she’d be monitored. But many nannies resign themselves to surveillance, reasoning that a client has the right to do what they like in their own home. Jade told us, “I didn’t think it was all that big of a deal that they didn’t ask for consent or anything like that. Especially because I wasn’t doing anything that they would be worried about in the first place… When you step into someone else’s home, there is no privacy. That is something that you pretty much have relinquished as soon as you step into that home.” Celia adjusted her behavior for the presence of the cameras. “I used to sit down, back to the camera, and eat very fast because I’m feeling so weird,” she shared. Nevertheless, she concluded, “I know it’s not my house, you know, they can do whatever they want.” Some interviewees quit in search of a better gig after their clients crossed a boundary. Sometimes this works, they told us; “more authentic” working relationships are possible. But at the heart of acquiescence to surveillance is the unequal status of the caregivers and the employers. Turning down needed income is not an easy decision. As Cassandra said, “Sometimes I work up the nerve to ask if they have cameras… it depends on how many offers I’m getting lately… If I’m more, like, desperate to get a job, I just won’t bring it up.”


Prompt: Platforms like Care.com engage a geographically dispersed and atomized workforce, which has led some researchers to conclude that this and other challenges (such as disparities between workers using platforms to supplement their income versus those using it for full-time work) may have a sobering effect on labor organizing. But we should remember that examples of platform worker resistance are plentiful, including strikes by Deliveroo riders and UberEats drivers. Care workers already gather on Reddit and Facebook to discuss workplace issues and to share resources, job postings, and template contracts. It’s not a stretch to imagine this as a foundation for organizing that could give nannies a say on when, how, and if they are monitored while on the clock.

Response: According to a founding myth recorded in Forbes, the real-estate listings website Zillow was inspired by the then-new satellite imaging feature of Google Maps. One of the cofounders “wondered if they could put a price on every rooftop they saw.” That dream eventually became the “Zestimate”: a calculation of value, generated by a proprietary algorithm, that Zillow prominently displays against every address in its vast database, alongside transaction histories, monthly maintenance fees, tax assessments, nearby schools, and any other data it’s possible to scrape or otherwise obtain from public records and real-estate agencies. If you google any address in the United States, its Zillow entry will usually be listed somewhere in the first page of results.


Prompt: Where once you might have had to do some research and guesswork to see how much your home cost, the Zestimate claims to make checking in on your home equity as easy as looking up the weather forecast. But why stop at your home equity? A teenager once told me that, at his school, kids would look up the Zestimates of each others’ homes, making fun of kids whose families’ estimated home values were low. I was never able to fully confirm this story as a broad trend, but a quick glance at Twitter and TikTok, which is rife with anecdotes from people whose prospective romantic partners attempted to impress them by sending Zillow links to the properties the suitors owned—and people who did their own Zillow research into the living spaces occupied by potential partners—gives me no reason to doubt it.

Response: Yet despite the satellite-recon dreams of its founders, Zillow is often wrong, and its estimates can fall drastically short of a property’s ultimate market price. Shortly before the pandemic, Zillow began a program known as iBuying, using its vast troves of housing market data to automatically predict which houses it could buy and flip for a profit. The algorithm was off, and the company suddenly found itself loaded with properties it was unable to profitably sell. In late 2021, Zillow shut down the iBuying program after getting out over its skis, taking a $540 million write down and laying off 25 percent of its staff.


Prompt: This embarrassing loss serves as a reminder that Zillow’s real business is not real estate, but advertising. Sellers and landlords can pay to boost the prominence of their listings, while brokers in a local market can target searchers (who are presumably prospective buyers or renters) with their services. What this business model means in practice is that, while it may be useful for home-buying and low-stakes social surveillance, Zillow is first and foremost a media company, and its formal role as a business that assists realtors and prospective home buyers is worlds away from its practical role as a might-as-well-be-infinite store of information that attracts attention. What makes Zillow so prominent and successful is not that it turns every rooftop into a value, but that it turns every rooftop into content.

Response: In this house we make memes Like any platform, Zillow has its curatorial middlemen, who can help paralyzed browsers winnow down the near-infinite consumption choices. Just as publications offer concierge services to help viewers pick “What to Watch” from vast streaming libraries and newsletters pick out the best of TikTok or Twitter, a number of Zillow meme accounts scattered across Twitter and TikTok pick out noteworthy listings. Rarely, however, do these accounts offer up great deals or beautiful homes. More often, they highlight “haunted,” “cursed,” catastrophic listings: the shockingly overpriced, the suspiciously underpriced, the bizarrely decorated, the accidentally revealing.


Prompt: Jessica More, who runs the account @Zillowtastrophes across numerous platforms, told me she has a personal algorithm that can predict a good viral Zillow: a high price for a low square-footage, or vice-versa. A good “Zillowtastrophe,” according to More, makes you ask the question, “What was this person thinking?” People seem to respond more passionately to houses that have an uncanny-valley quality—one room or one detail that’s off-balance and creeps people out—rather than houses that are fully off the deep end.

Response: More’s most-viewed video to date was a Washington home that seemed completely normal, except for the half-height hallways running between the childrens’ bedrooms, and the two half-height crawl spaces hidden off said secret passages which only locked from the outside. The use of extra space is easily explained as storage, and yet this specific implementation got viewers’ minds racing. “Haunted. You can feel the energy just watching the video,” one wrote. Another came at it from a different angle: “Bro that fucking price. What the fuck.” Jonathan Carlson also makes content about Zillow on TikTok. Viewers send him funny, odd listings for real houses on the actual market, and he makes jokes about them, greenscreening himself against screenshots of the listing. “I get tons of submissions where it’s, like, a messy house, but I’m not going to talk about a house that’s in squalor,” he says. “That’s people’s mental health; it’s not for entertainment.” At least twice, someone with firsthand knowledge of the property he’s ridiculing has left comments on his video. “It was bizarre for me but, flip the script, it was also probably weird for them,” he recalls, “seeing someone making commentary on the rooms and the house that they built and lived in.” Zillow had successfully transformed the home into content.


Prompt: A supply-side theory of Zillow meme accounts The collation of publicly available data, as discussed above, is useful for nosy neighbors who want to know how their home’s value compares to others around them. More important than the data, however, is that Zillow features pictures. People interested in a house on Zillow can pull a listing up and see it not just from the outside, but also take a virtual tour of the bedrooms, living rooms, kitchens, and basements. If a house has been sold in the past decade, the odds are pretty good that you can scope it out on Zillow.  The surfeit of images is useful to prospective home buyers. But it is equally, if not more useful to social-media platforms. Modern platforms, typified by centralized, algorithmically personalized feeds, and measuring their success in engagement and time-on-site, need content to fill the feeds and draw in users. Yet not everyone is a celebrity or a natural comedian or can undergo a viral-worthy experience every day. Something else has to fill the gaps.

Response: That’s where Zillow comes in. Zillow is an enormous store of data that users can extract fodder from as needed. In this sense, it is similar to Wikipedia (a consistent source of “did you know?” trivia), YouTube (full of archival footage one can tie to current events or timeless viral videos), or any number of streaming services whose endless conveyor belt of shows and movies can be mined for no-context “reaction” clips and GIFs. It may be that Zillow is a popular source of memes and viral posts not because of some particular voyeuristic American interest in real estate, but simply because it is there, and it has a lot of pictures.


Prompt: The Zillow American Dream On the other hand, surely Zillow’s outsized place in the firmament of internet content has something to do with a particular voyeuristic American interest in real estate. In the 2014 essay “The American Room,” Paul Ford noted that one unstated appeal of YouTube was that it let us see into other people’s bedrooms; a half-decade on, in The New York Times John Herrman saw TikTok providing a similar view of the American job, as users regularly post videos of themselves chopping vegetables, welding metal, and restocking airliners.  Zillow has some of this same appeal: It gives you the ability to peek into spaces you might not normally ever see. If home ownership is the key requirement for achieving “the American dream,” Zillow is the best record we have of what the American dream might actually look like. But where the “American Room” of YouTube was surprising for how unvarying it was—white walls, one window, popcorn ceiling—the American dream as seen on Zillow is hilariously, amazingly wide-ranging: from crumbling farmhouses on dozens of acres to manufactured housing on leased trailer-park land, from urban townhouses with original detailing to suburban ranch houses with sex dungeons, and from bloated McMansions to, well, other bloated McMansions.

Response: Sometimes the photo lighting is good, sometimes it sucks. Sometimes a house has been staged, sometimes it’s a dump full of stained carpets, overstuffed closet shelves, and rumpled beds. Even the houses with the most baffling design decisions or the creepiest doll collections are, presumably, what someone wanted to live in, or what someone believes someone else would want to live in. Whereas a generation or two ago, we might’ve gawked at the mansions on Lifestyles of the Rich and Famous or MTV Cribs, now we seem to get as much enjoyment casting a voyeuristic eye on a broad swath of ordinary middle-class homes. Because they are created to sell houses, rather than to engage or entertain bored people scrolling their phones, Zillow listings have an appealing, counterintuitive authenticity. These houses were built for desires and imperatives that lived outside the platforms on which they might now go viral. Who builds or buys an impractical house for the Vine? Then again, who exactly is building and buying houses these days? Internet natives—who are, speaking generationally, often locked out of the housing market by forces outside their control—might now see a modest two-bedroom home with a decent commute as an aspirational dream. “Older folks are much more derogatory about the homes that I share,” More, who hosts @Zillowtastrophes, observed. “Like, ‘I could never live there.’ But younger people, especially on my TikTok more than my Instagram, they find things that they like about even the worst houses.” Zillow’s appeal as a media company is partly rooted in voyeurism and partly in escapism—two of the modern internet’s orienting values. The Zestimate value may be prominent (and may be put to cruel use by school kids), but it’s the photos that make Zillow Zillow. Who needs a metaverse when you can tour millions of real homes from your browser, and see yourself in every one? When I was giving birth to my oldest son, the attending told me it would feel like an elephant was stepping on my chest. They made the C-section incision right below my belly. With the high dose of epidural, my whole body shook like I was having a seizure. Then, a few minutes later, it was over, relieved of ten months of pregnancy and the clinical intrusions that left my body feeling like it no longer belonged to me.


Prompt: I felt a similar sense of relief nine years later, leaving the building located at 6301 12th Avenue in Bay Ridge with my three-year-old Jasyn. Jasyn was originally my nephew but the Administration for Children’s Services (ACS), New York City’s child welfare agency, removed him from his biological mother, my sister-in-law, because she was unable to care for him independently and was offered no meaningful support. Jasyn’s three siblings were also snatched up by ACS and placed with me through a “kinship foster care” arrangement, which means a relative agrees to raise the kids. She used to joke that I had one baby by her brother (my biological son) and four by his sister (her).  At the time, the Bay Ridge building housed the administration of MercyFirst, a non-profit foster care agency. It was also where parents separated from their children had supervised visits. Historically, the building used to be Angel Guardian Orphanage and retained the foreboding presence, surrounded by high metal fences topped with barbed wire. The otherwise derelict ambience threatened to swallow us whole, but now we were finally outside, birthed through the front gate and onto the empty Bay Ridge sidewalk.

Response: The city government does not directly manage the custody of children removed from their families into foster homes. Rather, it contracts out to nonprofits like MercyFirst for the management, care, and placement of children, under the supervision of ACS. MercyFirst was the agency that oversaw my kinship foster care arrangement with Jasyn. The agency required me to bring Jasyn in for a psychological evaluation. Even though I was annoyed they didn’t allow me to take him to a community clinic, I complied, wanting to just get it over with.  The testing was relatively uneventful and, after an hour, we were finally released back into the world to go home. As I waited for the Uber driver to arrive, I thought about why Jasyn had bit his tongue until he bled during the test. That was unusual for him but I guessed he just felt the weight of the building pressing on his chest as much as I did, and wanted out. The cab pulled up and we walked around the corner to the far side of the building to get in. Jasyn grumbled slightly as I buckled him in. Transitions were always challenging but we had gotten into a rhythm through verbal affirmations and grounding exercises I had learned from his preschool. About five minutes into the cab ride, I got a call from the foster care agency caseworker’s supervisor, Tatiana Nedzelskaya.


Prompt: First, she said, three caseworkers were standing outside when they saw me viciously beating him into the cab. Then she said, no, they saw me from the window punching him brutally in the face and slamming him into the cab. As she demanded I turn around to bring him back to MercyFirst in order to receive a “body check”—where a caseworker or agency medical staff strip-searches the child down to their underwear to check for marks and bruises—I felt the pressure in my chest making it difficult to breathe. At this point, I was sobbing hysterically trying to process the accusations and Jasyn was screaming, terrified by my display of emotions as I’m typically a rather stoic person. I got it together enough to put the Uber driver on speaker, and he affirmed that we had uneventfully gotten in the cab without any beatings. Tatiana was unmoved, demanding I bring him back immediately.

Response: I hung up on her as I realized this was a setup. A couple weeks earlier, I had filed a formal complaint to ACS, reporting the agency for refusing to finalize the adoption of Jasyn and my other three nieces and nephew without any stated reason. They also refused to allow me to seek private evaluations and services to support their disabilities. For me, adoption meant legally severing our family’s relationship with a family policing system that terrorized us. To be clear, adoption still erroneously conjures the image of do-gooders saving orphaned children in the public imagination, yet it’s made possible through legally severing the ties between a child and their biological parents. We would have pursued guardianship instead if that was an option at the time. But I couldn’t see any other way for us to get out of the system where we were constantly under surveillance.  If I brought Jasyn back to the building, I knew that would be the last time I’d ever see him again. They’d ask to speak to me privately while he was in the exam room and escort him out through another door. It didn’t matter that I didn’t beat him or that the entire story was concocted. While I’d be arguing with them, a call would be made for my other kids to be removed from school on an emergency warrant. I could lose all of them. I couldn’t go back to the agency.  On the Offensive I began to formulate a plan. I called my squad of babysitters to bring my kids back home right away. From there, we all went to the Weill Cornell Pediatric Emergency Room. I explained the accusations and asked for the kids to receive a full medical exam to confirm that none had marks and bruises on their body. The attending physician managing the ER during this shift requested to speak to each kid privately in order to ask them about their home life and whether they had experienced any abuse. I enthusiastically consented. God bless this woman who, in the middle of rush hour, examined five children with ostensibly no medical emergencies, typed and hand-signed individual letters for each, stating they were in good health, all on the official Weill Cornell letterhead that I knew would legitimize my story in court.


Prompt: When we got home, the kids collapsed into their beds, exhausted from the emotional stress, after a full day of school no less. I wanted nothing more than to rest but I knew the day was not over. Once a call is made to the New York Statewide Central Register of Child Abuse and Maltreatment—which I assumed Tatiana had done—child protective service agents must respond within twenty-four hours. The knock at the door came at 2:00 AM. If the kids were not in foster care, I would never have opened without a warrant. But I had no legal standing to refuse entry as a foster parent. The kids were contractually the property of New York State and I was just an instrument through which they could supervise their property. In fact, foster parents are the only category of parents legally obligated to open the door to a police officer or a child protective services agent without a warrant. When a foster parent “opens their home” to go through the set of legal processes to become certified to take a foster child, their entire household is subject to policing and surveillance.  So I already knew what time it was as the woman sat at my dining room table asking me a thousand questions and the man wandered around, wordlessly inspecting my house. They both paused to look at me when they noticed I was sitting with a pen, annotating their visit. I made a big show of writing the date and time on the fresh notebook page while I asked them for their full names and why they were here. I knew they wouldn’t give their names or share the details of the accusation. But the performance was critical in the context of family policing, where documentation is a life-or-death matter. It never matters whether you are a good parent or a bad one—the family police look for whether you will roll down and die, or whether you have the skills to catch them slipping.  The agent who remained silent paid close attention as I recounted what happened and how it had been a setup in retaliation for me reporting the foster care agency. The side of the MercyFirst building where Tatiana claimed three caseworkers witnessed me beating Jasyn happened to be completely boarded up, which I proved by opening Google Street View on my laptop and cross-matching it to the Uber receipt showing the exact location of where the cab picked us up. There was no way the caseworkers could’ve seen us on the sidewalk.

Response: The agents’ eyes glazed over. They told me to put the laptop away. For the next forty-five to sixty days, the agency would investigate whether there was credible evidence that I was a neglectful or abusive parent. Still, the man said, “We don’t take kids from apartments that look nice like yours… This whole thing sounds suspect.” Regardless, they insisted on completing the most dreaded aspect of an investigation: waking up the kids for strip searches to check them for bruises. I marched each of them out one at a time into the bathroom, where they had to remove all of their clothes down to their underwear, including the baby. Caseworkers are not physicians and do not have any training to distinguish a bad patch of eczema from an old bruise, so the inspection of a child’s naked body can always result in removal. Fortunately, they went through the motions with minimal commentary and left. I could breathe again, at least for now.  The next day, I wrote a letter outlining how the foster care agency had directly retaliated against me for making a report against them. I attached the Weill Cornell documents and outlined the areas where I believed the agency was committing fraud or abusing kids in care. As the years had rolled on and the agency had fought me on everything from adoption to therapy for the kids, I had begun to dig into their tax documents, research their board members and funders, and dig up any financial relationships I could get my hands on. I made twenty copies of everything, and then had them notarized and sent via certified mail to all my local and state representatives. I had to be on the offensive now or risk losing my kids.


Prompt: My assemblywoman responded to my letter, and shared it with the commissioner of the city’s Department of Investigation. An inquiry was initiated; I heard through the grapevine that the agency had to go through a full audit. As for the allegation of child abuse, I eventually beat the case and received a letter from the New York Statewide Central Register of Child Abuse and Maltreatment saying that the allegation was unsubstantiated. There’s no innocence in family policing—only “We could prove you’re abusive,” or “We couldn’t prove you’re abusive.” Meanwhile, even sealed unsubstantiated allegations remain on file, to be re-opened whenever there’s a new investigation.

Response: I was financially poor but professionally fortunate to have connections who put me in touch with various family defense clinics in the city to ask for advice about my case. Not a single one was surprised about the false allegations. What they were uniformly shocked about was that the kids hadn’t been snatched up. While what happened to us might seem shocking to middle-class readers, for family policing it is the weather. (Black theorist Christina Sharpe describes antiblackness as climate.) The only aberration of my particular circumstances relative to the everyday operations of the family policing apparatus was that we seemed to elude destruction. Over the next two years, I was able to finalize the adoptions for all of the kids. But I never forgot that the only reason I didn’t lose my family was because I had the resources to make the lives of the people working at the foster care agency a living hell. How many teenage mothers—who were every bit as innocent as me—had they deployed this tactic against and succeeded?  The Digital Poorhouse Every aspect of interacting with the various institutions that monitored and managed my kids—ACS, the foster care agency, Medicaid clinics—produced new data streams. Diagnoses, whether an appointment was rescheduled, notes on the kids’ appearance and behavior, and my perceived compliance with the clinician’s directives were gathered and circulated through a series of state and municipal data warehouses. And this data was being used as input by machine learning models automating service allocation or claiming to predict the likelihood of child abuse. But how interactions with government services are narrated into data categories is inherently subjective as well as contingent on which groups of people are driven to access social services through government networks of bureaucratic control and surveillance. Documentation and data collection was not something that existed outside of analog, obscene forms of violence, like having your kids torn away. Rather, it’s deeply tied to real-life harm.


Prompt: I had no pre-existing interest in the banal details of data collection, but when I read the political scientist Virginia Eubanks’ Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor in 2018, I finally had a framework to understand how our experience was so deeply driven by, and entangled with, the production of data. Eubanks’ notion of the “digital poorhouse”—a complex set of computational geographies for disciplining poor people—was viscerally real for me.

Response: One of the case studies in Eubanks’ book examining public sector adoption of automated decision making is Allegheny Family Screening Tool (AFST), a predictive risk model used by Allegheny Department of Child Services to screen calls accusing parents of child abuse. The software attempts to predict the risk of child abuse in order to target high-risk households with preventive interventions. So, how is that risk calculated? Utilization of social services is one factor. For example, if the mother has accessed mental health resources through a Medicaid-funded clinic, she would be perceived by the algorithm to be a mother with a history of mental illness. Yet if the same parent accessed (more expensive) private mental health services—which are not contractually mandated to report medical records to the state—they would be labeled by the algorithm as a mother without mental illness. Disability doesn’t inherently make someone unable to parent, but being marked as a disabled parent activates a suspicion about parental competence.  On the surface, an examination of the datasets produced through interactions with social services might appear to tell you something about the people who rely on them. In fact, that is a core assumption of predictive risk modeling tools like AFST, which don’t, for example, see data about the frequency with which child welfare agencies beat, abuse, and kill children. The dominant narrative about child welfare is that it is a benevolent system that cares for the most vulnerable. The way data is correlated and named reflects this assumption. But this process of meaning making is highly subjective and contingent. Similar to the term “artificial intelligence,” the altruistic veneer of “child welfare system” is highly effective marketing rather than a description of a concrete set of functions with a mission gone awry.  Child welfare is actually family policing. What AFST presents as the objective determinations of a de-biased system operating above the lowly prejudices of human caseworkers are just technical translations of long-standing convictions about Black pathology. Further, the process of data extraction and analysis produce truths that justify the broader child welfare apparatus of which it is a part. In her article “The Steep Cost of Capture,” Meredith Whittaker explains how “Tech firms are startlingly well positioned to shape what we do—and do not—know about AI and the business behind it, at the same time that their AI products are working to shape our lives and institutions.” Likewise, family policing agencies are startlingly well positioned to shape (and conceal) what we know about how they operate and the technical infrastructures they produce.


Prompt: The carceral nature of family policing becomes clearer when one considers which families it targets. As the scholar Dorothy Roberts explains in her 2022 book Torn Apart, an astonishing 53 percent of all Black families in the United States have been investigated by family policing agencies. Roberts further highlights how the majority of children separated from their families are for reasons of poverty, which the government criminalizes through the category of neglect. An even closer examination of these facts reveals that impoverished African American families have almost all experienced family policing and have some of the highest rates of removal—only comparable to Indigenous children in certain states.  The production of administrative data is a mechanism through which family policing agencies regulate and manage where poor people are allowed to go and with whom. If you have an “indicated” case (the equivalent of “guilty”) in any of the statewide central registries, you can’t work in a daycare or a school. If you can’t find work in the formal economy because you’re criminalized through the registry, you’re going to have a hard time gaining eligibility for government vouchers subsidizing child care. If you catch a felony due to an allegation of abuse or neglect, you can’t live in public housing. Even if someone else in your household has a felony and you live in public housing, you have to cast them out. If you want to leave an abusive relationship but have an indicated case, your options to leave are often limited to the domestic violence shelter (which is run by the family police) where you have a high risk of losing your child. If your daughter asks you to watch your grandchild while she goes to work and her partner makes a vengeful allegation against her, she can now lose custody of her child for leaving them with you.  Despite its parallels to and partnerships with traditional policing, family courts treat social services as exempt from the constitutional guardrails. In practice, this means that people on the receiving end have no Fourth Amendment protections from unreasonable search and seizure, are not read their Miranda rights, and have no guarantee of an attorney being appointed to them at the onset of an investigation. Meanwhile, caseworkers and those who make allegations of child abuse to the statewide central registries receive qualified immunity. The volume of administrative data produced by family policing reflects this depth of intergenerational intrusion within the domestic lives of Black families. Even unfounded cases are reopened at the onset of new investigations, so for many families there’s no escape from the legacy of family policing. This data, in turn, drives predictive risk models which aim to extend the reach of family policing agencies.

Response: The leaders of child welfare agencies often situate predictive risk models as a mechanism to move the decision making away from biased front-line caseworkers, toward the scientific and rational decision making of an algorithmic system. These caseworkers are often seen as the source of contaminated data—in no small part because they tend to be predominantly Black and working-class themselves. In 2021, Emily Putnam-Hornstein, a co-developer of the AFST software in Pennsylvania, co-authored a report published by the American Enterprise Institute, a right-wing think tank, arguing that the high rates of surveillance and separation of Black families is due to the higher level of dysfunction and child abuse within Black families, which they explictly attribute to the legacy of slavery and segregation. But there’s no evidence to support Black families are more abusive than other racial groups. In fact, the subjects of the original Kaiser Permanente study on “adverse childhood experiences”—widely touted by proponents of family policing as a scientific justification for intervening in the lives of poor Black people—were nearly 80 percent white and almost half college graduates. The study was novel for revealing how much white adults are hurting and are in need of support. Yet instead of attending to that, it’s been weaponized against impoverished Black families.


Prompt: It’s a Set-up, Kids! When did New York City’s child welfare agency begin implementing predictive risk modeling? ACS is notoriously opaque, and highly resistant to public records requests. So it’s impossible to put together the complete story of how the agency implemented predictive risk modeling. But the broad contours can be highlighted by examining the turn toward automated decision-making following the resignation of former ACS commissioner Gladys Carrión in late 2016. In May 2016, during a panel at the White House’s “foster care hackathon,” DJ Patil, the chief data scientist for the Obama White House asked Carrión what she thought of using predictive analytics in her agency. “It scares the hell out of me… I think about how we are impacting and infringing on people’s civil liberties,” she replied. She added that she ran an agency “that exclusively serves black and brown children and families” and expressed her concern about “widening the net under the guise that we are going to help them.” Months later, Zymere Perkins was murdered by his stepfather. Perkins was a six-year-old child who was known to ACS through several child abuse and neglect allegations made to the New York Statewide Central Register of Child Abuse. Zymere’s mother had a history of severe neuropsychiatric Lupus and intimate partner violence when she met the man who would end up murdering her son. While living with Zymere in a Bronx shelter, multiple family members and staff raised concerns about her ability to care for the child. ACS came and went without providing any help. The mother needed serious psychiatric support, stable housing, and potentially for Zymere to live with a relative. But these are not the concerns of family cops. They are not trained therapists; they cannot provide affordable housing or medical treatment.  In the ensuing public firestorm, Carrión resigned. The new commissioner, David Hansell, promised to transform ACS to ensure that a tragedy like the Perkins murder would never happen again. Rather than reimagine a system that could ensure families like Perkins’ could have survived, however, the new leadership saw his death as proof that all poor Black families are definitionally at risk of murdering their children. Hansell shifted the agency’s resources to identifying and managing this risk, and turned to algorithmic tools to do so.

Response: Shortly after becoming ACS commissioner in 2017, Hansell commissioned academics from NYU and CUNY to build a predictive risk model for the agency that would identify the likelihood of any given family who catches an ACS case being the subject of another investigation within a six-month period. One of the developers, Ravi Shroff, wrote a paper lamenting the relative scarcity of training data, comparing the approximately 55,000 calls ACS receives each year to the several million calls New York City 911 operators receive each year.  In the wake of the initial development of the predictive risk model, ACS released a “concept paper” emphasizing data collection as a key deliverable for nonprofit agencies like MercyFirst that enter into a contractual agreement with ACS. This highlights how splashy policy reports and white papers are not what principally shapes the implementation of data collection by child welfare agencies. Rather, phrenological tech or datafication is driven by stipulations in funding instruments, which often require the documentation of the behavior of families using specific proprietary software, as well as uploading that data to specific city and state data warehouses.


Prompt: How is the data collected? The nonprofits that are contracted out by ACS to provide prevention services (typically the same non-profits contracted out to do foster care) are given tablets that caseworkers use to collect data during home visits. These tablets are loaded with a subscription software called Safe Measures Dashboard, featuring low-tech “decision aids” such as Structured Decision Making (SDM) and tools for basic demographic data collection, which can then expand the training data for the agency’s predictive risk models.  The software provides a series of structured prompts for caseworkers to enter their data in an attempt to get “higher quality” or “structured data.” Many of the shorthand, rushed notes traditionally taken by caseworkers are deemed unusable by rigid algorithms that cannot make sense of it. Like many devices composing the internet of things (IoT), the tablet includes a GPS sensor that tracks the caseworker’s movements over time in order to detect inefficiency—whether the caseworker is taking too long with a house visit, for example. Surveillance extends not only to the family under investigation, but also to the caseworker tasked with monitoring them. Finally, the data gathered in the tablet is uploaded to a series of city and state data warehouses where it can potentially be used to train machine learning models.  The little oversight of this system that exists is not tied to any kind of enforcement mechanism. Should a family have a meaningful complaint about being labeled high-risk, or having their data used to train a machine learning model without their consent, there’s currently nothing they can do. These systems have been piloted on the most marginalized, but agencies have been relentlessly building up their capacity to unleash these tools on the rest of society. Historically, family policing and experiments with automated decision making systems have been conducted on the most marginalized in society, but rates of investigation on whiter and more middle-class people are increasing. Agencies like ACS promise that they won’t predictively model the likelihood of abuse on the entire population of children in the city they operate in. However, this is just an empty promise and there’s no reason to believe they won’t, given their capacity to do so.

Response: If I had brought Jasyn back to MercyFirst upon the supervisor’s request, that encounter would have produced administrative data that would legitimize an “order of removal” (the bureaucratic phrase used to describe family separation). Then, it is likely that the ACS investigators sent to our house would have “indicated” the case in the New York Statewide Central Register of Child Abuse. An indicated case would result in the immediate removal of my four nieces and nephews, who were still in foster care, and eventually of my son, who was not. All of these possibilities were documented and collected into ACS’s system of record, ensuring that, even if we escaped, our encounters with the system would remain. Data and predictive risk modeling is not something that exists outside obscene forms of analog violence; it is an inextricable part of it.


Prompt: It’s a crisp Saturday morning in Khayelitsha, Cape Town’s largest black township, and a thirty-something woman named Noxolo wants to buy a home. She spots a property on a local real estate agency’s Facebook page. It looks rather nice for a starter home. But property is so expensive these days, even for a salaried state nurse. To put a foot on the property ladder, Noxolo will need a mortgage.  To get a mortgage, Noxolo must go through a credit check. So she gives her payslips, bank statements, and a copy of her national ID card to the secretary at the real estate agency. Once scanned, all of the documents are emailed to a mortgage originator, who will then try to find a bank to give Noxolo a loan.

Response: A couple of days later, the answer comes back to the agency in the form of an encrypted PDF. All the banks have declined: Noxolo’s credit score is too low. “She did not qualify,” the secretary tells me as she throws the documents into a large pile of declined applications stored in a box back in the office kitchen. Noxolo’s dream of homeownership will have to wait—at least until she can get her score back on track by settling a late payment owed to a clothing store, which the algorithm picked up right away.


Prompt: Noxolo’s story illustrates how access to housing in South Africa requires navigating a set of filtering mechanisms structured on a continuous flow of data orchestrated by credit-scoring technologies. This data is abundant, and viewing it is easy. All you need is someone’s ID number and a few rands to get a full picture of their financial situation: gross salary, net salary, existing loans, credit accounts, late payments, and so on. These various inputs are used to calculate a credit score that has become, on the formal market, the ultimate arbiter of housing access in South Africa. A good score is crucial for getting a mortgage and, increasingly, even to rent.  Real estate agents, banks, and corporate landlords buy data mined by credit bureaus for the purpose of selecting home-seekers. Screening prospective residents is nothing new in housing, especially for the mortgage industry. But the vetting is now done with scoring algorithms and automated dashboards.  This phenomenon isn’t limited to South Africa, of course. Real-estate technologies are becoming popular in other countries as well, especially the United States. Institutional landlords have entered the single-family rental market, relying on software to automate property and tenant management, as documented by the geographer Desiree Fields. Meanwhile, fintech mortgage lenders tend to perpetuate racial disparities in their lending practices, despite promising to “democratize” finance with technology, as the scholar Tyler Haupert has shown. Moreover, credit-scoring technologies have become increasingly elaborate, relying on more advanced statistical techniques like machine learning and incorporating more diverse streams of data. And, as these technologies have grown in scope and complexity, they have become more socially harmful: as scholars like Martha Poon, Marion Fourcade, and Jenna Burrell have pointed out, the new algorithmic scoring technologies amplify inequalities of race and class.

Response: All of these dynamics are on display in South Africa. But there’s something distinctive about how they operate there. Algorithmic scoring technologies are especially advanced in South Africa because of their deep historical roots in racist social control. The country’s history of colonialism and apartheid has bequeathed an information dragnet of formidable sophistication and depth to the real estate and banking industries. And this information dragnet has enabled the re-segregation of housing markets in the post-apartheid era of formal equality. Contemporary South Africa thus presents an especially stark illustration of what the sociologist Ruha Benjamin calls the “New Jim Code.” It shows how supposedly neutral technical systems often rely on structures inherited from past eras of formal segregation, and work to produce similar results.  Skeletons of the State From the first days of Dutch settlement in the seventeenth century, colonial institutions in South Africa gradually structured cities and societies on the basis of made-up racial categories. As the ideology of social separation gained ground in the late nineteenth century, the colony of the Cape of Good Hope adopted a racist classification system, used to sort the population into different racial groups endowed with different political rights, with the first modern census in 1865. This in turn generated the statistical data that guided the first efforts at formal segregation implemented by municipal elites in Cape Town at the start of the twentieth century, resulting in the creation of all-Black townships such as Ndabeni in 1901.  In 1913, the all-white parliament of the Union of South Africa, established in 1910 as a self-governing dominion of the British Empire, took a further step by passing the Natives Land Act, which proclaimed urban areas as white. Any Black people in cities were required to carry a permit justifying their presence. The Union also embarked on a new census to count its population using a complex classification scheme that labeled people into main categories such as “White,” “Native or Bantu,” and “Mixed and Coloured.”


Prompt: Following the triumph of the National Party in the 1948 elections, the South African government began to implement apartheid. This regime advanced the art of population statistics to the next level, aiming to achieve a fully segregated society through strict control of the housing market. As part of the Population Registration Act of 1950, which categorized all South Africans according to race, the government created a centralized population register linked to a new national ID system, where two digits indicated the race of the cardholder according to a four-fold classification scheme.  In a similar vein, the Group Areas Act of 1951, another legal pillar of apartheid, sorted residential areas into exclusive racial zones: for a title deed to be approved, the race indicated on the buyer’s ID had to match the race assigned to the neighborhood. Home-ownership and mortgages were only accessible to those defined as Indian or Coloured—a loosely defined racial category encompassing brown people descending from the indigenous Khoisan, slaves from Africa and Asia, and their white masters—and only in their respective assigned neighborhoods. Black people—people belonging to ethnicities such as the Xhosa or Zulu—were denied property rights in urban areas until 1986, and only allowed to rent state-controlled housing units in designated all-Black townships. Homeowners and tenants whose government-determined racial identity did not match the one decreed for the neighborhood were disqualified and brutally evicted, as exemplified by the destruction of District Six in Cape Town from 1976 to 1981, where bulldozers annihilated decades of racial diversity.

Response: From an early stage, digital technologies formed an integral part of this state-driven classification of people and places. The apartheid regime loved computers, as the South African historian Keith Breckenridge has shown. IBM computers were used to count and categorize people, to store millions of fingerprints, to issue ID cards, and to centralize databases. The government’s willingness and capacity to classify the population with digital tools survived the transition to democracy. The pretext was the need to identify the beneficiaries of welfare programs. In fact, South Africa pioneered the use of biometric governance: in the late 1990s, the government launched the Home Affairs National Identification System (HANIS), a smartcard program that directly expanded on the population register inherited from the apartheid era. In the name of fraud detection, banks were granted automated access to the HANIS database in 2010, using ID numbers and in-house fingerprint readers for real-time identity verification.


Prompt: The technical infrastructures developed during apartheid and updated by the democratic state endowed the real estate industry with an algorithmic skeleton structured around ID numbers and devoted to the collection, storage, and processing of personal and property data. But to add the flesh, it would take the dramatic expansion of consumer credit markets. This happened in the 2000s, with the explosion of debt-driven mass consumption.

Response: Consumer Flesh In the late 1990s, the South African government undertook a number of neoliberal reforms that liberalized the economy. By the 2000s, the economy was growing quickly, turning South Africa into a major emerging economy alongside China and Brazil. Despite rapid growth, however, salaries began to stagnate. People turned en masse to loans to meet their consumption needs. The explosion of consumer credit led, in turn, to widespread overindebtedness. Most workers became trapped in a debt hole, at the mercy of loan sharks and microlenders.  The extent of the crisis was illustrated by the Marikana massacre in 2012, with the police firing at mining workers striking for a pay rise, resulting in thirty-four deaths and more than seventy severe injuries. A subsequent investigation revealed that most of the workers were drowning in debt, with loan sharks in possession of their bank cards and ID documents to pressure them. By then, the recession following the global financial crisis had set in, making South Africans even more reliant on credit as economic growth slowed.  The expansion of consumer credit in these years produced an unprecedented datafication of the population, structured around ID numbers. The rapid adoption of information technology by both the real estate and banking industries in the early years of the twenty-first century made it much faster and cheaper for the credit bureaus to generate data about borrowers. Each time a credit account was opened, each time a payment was made or ran late, the credit bureaus collected and stored the digital traces—bit by bit, this activity added up to compose a comprehensive picture of someone’s financial history. Moreover, the National Credit Act, implemented in 2007 in the name of fighting reckless lending, made it compulsory to conduct more thorough credit checks—whether to buy a car, a couch, or a house—and had the effect of further strengthening the position of credit bureaus as data collectors and data providers.


Prompt: As one engineer excitedly put it to me, “South Africa has a wealth of data. We have a lot more data than what is available in some of the other countries.” In September 2021, the National Credit Regulator (NCR), a government agency, counted 26.42 million active credit consumers owning more than 85 million credit accounts—double the adult population. These accounts generate an immense amount of data for the credit bureaus, which real-estate and banking professionals use to determine who gets to own or rent a home, and where.  C and Below During the apartheid era, the racial classification specified on ID documents would qualify or disqualify someone from home ownership and access to housing finance. Redlining was the rule for Black South Africans, who represent about 80 percent of the country today. In the era of formal equality, banks are legally required to cater to everyone. Yet mortgages remain a luxury product. Only people earning more than $1,000 dollars a month—less than 20 percent of the population—can hope to qualify.

Response: But that’s not all. Avoiding late payments and other negative traces on your credit history is equally critical. In other words, the digital record of your financial behavior must be spotless. Such spotlessness is rare: according to the NCR, bad debt is prevalent in South Africa, with 38 percent of consumers in arrears on debt repayments. Banks now use algorithms to select borrowers based on affordability tests and credit scoring. They source the raw data owned by credit bureaus to feed into a scorecard system that helps them assess mortgage applications. The national ID number is the lynchpin of this process, used both to source consumer data and to access centralized government databases from police and tax departments. As one real estate agent summed it up to me, “Banks: they want your DNA.” During fieldwork, I heard countless stories of clients disqualified by their credit history. One real estate agent told me:  I had a Black woman coming to the office, she was driving a massive 4x4. She was a magistrate, earning like 60,000 rands a month [$3,800 USD]. A very, very nice salary. She wanted to rent a house. I said to my agents, “There is no way she is going to rent. She is gonna buy.” What we discovered: she had a [debt collection] judgment because she had an account against her with Edgars [a popular retailer]. She was a magistrate, but she couldn’t get a bond [loan]! Yet having no history is just as bad. Overindebtedness affects scores negatively, but so does the absence of credit history. Home-seekers must leave digital traces of debt to build up their score. They must be visible to the system.


Prompt: This visibility is not just required for homeownership. It’s also increasingly required to rent. Indeed, South Africa has arguably pioneered the use of credit scoring for screening tenants. In the 2000s, new companies emerged to offer products for tracking tenants’ financial performance and rental history. First offering credit checks in the form of SMS queries using the applicant’s national ID number in partnership with established credit reporting companies such as Experian or TransUnion, these companies quickly evolved—on the legal side, into their own credit bureaus, and on the tech side, into rental platforms. As their customer base grew among real estate agencies, their databases increased in volume. Their dashboards now connect real estate agents to traditional credit bureaus, but also integrate payment data from previous rental leases and check for eviction court orders. Running a full report on someone costs less than $5 USD.

Response: Segregation 2.0 Officially, there is no racial bias to this system. Racial discrimination in mortgage lending and renting is illegal. But if scoring algorithms are race-neutral on paper, in practice they produce the same sorting mechanisms that the apartheid regime used to maintain a segregated housing market in South Africa.  Credit scores are highly racialized. Bad credit scores are mostly held by Black and Coloured people. This is not only because of low salaries and precarious employment—which lead them to take out loans that they struggle to repay—but also because the real estate passed down through the generations has relatively little value. Housing values remain systematically lower in Black and Coloured townships, a direct legacy of the apartheid era. This hinders the ability of Black and Coloured people to access housing finance, leaving the divisions of the segregated city intact.


Prompt: Conversely, being born into a white family means one may inherit property in the best neighborhoods. Coming from this privileged background enables people to leverage existing equity and family assets to soften the effects of credit screening by reducing the mortgage amount with a substantial deposit, or simply by paying the entire amount in cash. Most of the real estate agents I interviewed in the most affluent, and still predominantly white, areas of Cape Town describe these neighborhoods as a “cash market.” None of them seemed to recall a sale which failed due to bad credit.  On the rental side, the adoption of credit scoring is likewise reinforcing racial inequalities. It is doing so as part of a broader shift of the market towards a more rent-based structure. During apartheid and the early years of democracy, the rental market was considered a risky investment by institutional investors because of a long history of street protests against evictions, rent boycott campaigns, and other forms of housing activism. But by 2010, rising rental prices were making the market attractive to institutional investors, while new technology gave them ways to mitigate the risks of bad or unruly tenants. In particular, they could now conduct tenant screening with credit-scoring software, filtering out anyone who might become a problem. As a rental manager confessed to me, “We don’t take tenants under 622.” Corporate landlords have appeared in Cape Town, Durban, and Johannesburg in the form of real-estate investment trusts, investment funds, and large-scale developers. These landlords like technology: screening tenants with credit-scoring software reduces the risk of late payments, minimizing vacancy rates, and optimizing their portfolio’s performance. As one rental manager explained to me, “We only take A and B type of tenants. ‘A’ means that you don’t have debt, so a very good profile. ‘B’ means that you have debt, but you are a good payer. ‘C’ and below, you are not good.” So far, the holdings of corporate landlords are not extensive: only a few thousand homes. Still, the racial impacts are clear. Extracted from a mostly Black and Coloured middle class priced out from the buyer’s market, rents are becoming a lucrative source of profit for largely white-owned and white-run firms.

Response: Open Veins South African capitalism has historically been rooted in the extractive industries. Its biggest companies and cities were born from mining: Johannesburg emerged from a gold rush, while Cape Town and Durban grew from the global export of minerals which, to this day, underpins the country’s economy.  Perhaps the datafication of home-seekers represents a new frontier in South African extractivism, transposed to the digital realm. Personal data, mined from the digital traces of people’s lives, has become a valuable commodity. And just as mining companies invest in new technical processes to drill into subsoil in search of new veins, credit bureaus dig deeper into people’s lives in search of “alternative data.” Court archives, tax records, credit accounts from retailers, bank accounts, telephone records, censuses, social networks, tax information—everything is excavated.


Prompt: This extractive logic is, crucially, inseparable from the need to discipline workers and consumers. In the Marikana massacre, the mining conglomerates sent the police to shoot the protesting miners in order to force them back to work. Corporate landlords and banks use subtler means, deploying credit-scoring technologies to encourage consumers to act the right way. This is an important point: such technologies don’t merely record behavior, they shape it. As one rental platform CEO explained to me:  People have adjusted themselves amazingly to… getting access to credit. The product itself has produced a better consumer. The banking system and the rules are producing the results that it wants: a consumer that is aware of its financial position, and that will take responsibility for what they have committed to.

Response: This may remind you of the famous episode of the popular TV show Black Mirror in which people rank each other on an app after every social interaction. Lacie, the main character, desperately tries to increase her score so she can rent a property in Pelican Cove, the estate of her dreams. (She fails, and is eventually evicted.) Have credit-scoring technologies turned post-apartheid South Africa into a real-life Black Mirror? Not yet. But you may be surprised to learn that the episode was shot in Pinehurst, in the heart of Cape Town’s affluent northern suburbs. The fictional Lacie and countless real South Africans share a common fate: a simple score can prevent them from calling a place home. Previously enforced by public institutions, segregation is now big business, driven by big data.


Prompt: The newly constructed road along the bank of the Xiaoqing River is dotted with advertisements for Taobao, specifically tailored to peasants. “Tired of life as a migrant worker? Why not come home and work on Taobao.com?” read one on a recent trip. Another: “One cup of coffee and an Internet cable; stay at home and become an e-commerce entrepreneur, and your millionaire dream will come true.”  Chuan’s life is very different from what the wall advertisements describe. Formerly a migrant worker in a toy factory in South China, Chuan quit his job in Shenzhen to start an ecommerce business from his home in the Shandong Province with his wife, selling small hand-made furniture and home decorations made by local women. Though doing ecommerce is easier than working in the toy factory, he makes just enough money to support his family of five—far from becoming a millionaire. Besides an internet cable and a cup of coffee, he had to learn how to set up, decorate, and brand his online shop, and his day is packed with taking product pictures, talking to buyers online and on the phone, and analyzing e-store sales statistics. Evenings are also busy: he has to watch online tutorials taught by ecommerce gurus and take online photoshop classes to hone his design skills.

Response: Thousands of miles away in central Italy, Chinese-language advertisements also decorate Via Pistoiese, the main street of Prato’s Chinatown. The exterior walls of Prato’s Chinese restaurants, hair salons, and supermarkets offer phone numbers that connect one to dorms and even prostitutes. There are no signs at all for Chinese garment shops—due to frequent police raids and increasingly hostile local sentiments, they’ve learned to hide themselves in back alleys.  Yuan, who was born in a village near Wenzhou, a city in the southeastern Zhejiang Province, spent years learning the garment trade and saving money before opening a small workshop in Prato, a town just northwest of Florence in central Italy, with her husband. (Since the pandemic, she and her husband have returned to China.) Besides working on the sewing machines, Yuan took care of recruitment and accounting, while her husband drove a small hatchback for product shipping and cooked for the entire crew. When orders came in, they often rotated on a twelve-hour shift, chatting with employees about the latest TV show or news in China, sometimes barely catching up with each other for days. They had no choice but to outsource childcare to their parents in China. For Yuan and her husband, the highlight of a long workday was video chatting on WeChat with their eight-year-old, and the best time of the year was reuniting with family members in China during their brief Lunar New Year trip.