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Owen maintains the mining rigs as they search for whatever cryptocurrency earns them the most per watt, but it is his wife Cassie’s trading that really keeps the family afloat. When not homeschooling the kids, she is glued to her computer, hoping to multiply their earnings by trading the cryptocurrencies that the mining rigs bring in for others she hopes will grow in value. Now that she has a few years of experience, she knows how to spot a “scam coin” and which forums to trust. Most days, she earns a profit.
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The children, who range from first grade to college, help with both sides of the business. The oldest daughter has been in charge of keeping the miners cool since she was fifteen. In exchange for fixing the fans, reapplying thermal paste, and maintaining airflow, she gets a cut of the profits. Other kids help with the trading. Those who aren’t legally old enough to have their own accounts use Cassie’s spreadsheets to simulate swapping crypto. Owen reads crypto articles aloud at the dinner table to stir up discussion, and even the seven-year old has an opinion on the family business. (She likes DigiByte, an obscure security-focused coin.) The Collins can keep everyone fed by mining and trading crypto, but only because everyone pitches in.
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We are in a situation where we must have mutual aid. We must send money back home. We must send money to Texas when the state has failed to take care of people. But in that must, we’re relying on these apps or these infrastructures that were designed not in service of us. Not to produce that livability, right? So how do we think about whether there is something qualitatively new manifesting in these technologies? What is new about predictive policing? Before the prediction, policing was still bad, still needed to be abolished, right? Is it just automating that same practice or is something different happening? I think about Virginia Eubanks’ comparison between the 20th century brick-and-mortar poorhouse and the present day digital poorhouse that is using algorithms. She emphasizes how the former geographically co-located Eastern European immigrants and Black Americans together—which some argue laid the ground for the Poor People’s Movement—as compared to the algorithmic sorting of the digital poorhouse which preempts that kind of cross racial solidarity or physical proximity. I feel like the way political subjectivities are formed in relationship to a state’s (often concealed) control of people’s movement through space is a theme of your work on Blackness and migration. Are there connections that you’re making in thinking about these examples? That’s really helpful. I love examples since I really appreciate having something to hold on to. Your question about this distinction makes me think of Cedric Robinson’s concept of racial regimes—that which does not want to be revealed, but by the very nature of its revelation, speaks the truth about the mutability of racial representations as historically uncertain. This is why the system of racial capitalism and the flourishing of white supremacy is specifically one of the things that pretends it does not exist, that there is no hand there building on pre-existing cultural forms with new technologies that emerge to retrench those processes. In this way, “new” technology hides the original intent and how those aims differentially structure our realities.
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Last month, when Facebook apps all went down—a possible distraction from the testimony of the company’s whistleblower Frances Haugen before the US Senate—I felt this regime acutely. I was trying to help support planning and get information about my grandfather’s funeral in Jamaica, and I couldn’t reach any of my relatives in the Caribbean and across the diaspora, who all use WhatsApp as the primary mode of communication. For a lot of my family, like millions across the Global South, staying in touch internationally is far too expensive over landlines, and VoIP services like WhatsApp have filled this need. During the temporary crash, I couldn’t figure out how to send money to them. I couldn’t figure out where they were physically so that I could then try to find out which cousin or which uncle or family friend or local pastor had a landline that I could attempt to reach. At that moment, I realized WhatsApp, owned by Facebook, was completely determining my ability to connect with my family, to grieve and support and organize my community in real time, with material consequences.
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In my case, the termed position is two plus two, meaning I have a two-year term and would be up for renewal for another two years. They can fire me within the first three months and they can decide whether or not to renew my term at the end of the first two years. Terms can be anywhere from a year to eight years.
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Termed employees are essentially civilians—I can’t apply for other competitive positions outside of my current role. When I’m done with this role, I have to apply again to a GS position and all of my government experience doesn’t count. When you say it doesn’t count, do you mean toward the qualifications for other positions or for benefits? All of the above. That experience essentially doesn’t exist. If I apply and get into a permanent position that is open to the public, which is some small percentage of the perm job positions, it could contribute to the pension calculation, if and when I retire. But that’s about it.
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Honestly, one thing that is interesting is that from a matching perspective we’re pretty gender- and orientation-agnostic. We don’t try to use the algorithms to pair people of certain identities with people of other identities—we really just focus on personality questions and preferences, then allow people to choose how they filter within gender identities. We want people to find other users who are great matches from a personality perspective, possibly in places that they didn’t expect.
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You’ve talked about having core principles when you think about the features that you’re willing to develop. In the role of CTO, how did you go about crafting the engineering and product teams around those values? Was there a set of core principles that you aligned around? Were there particular qualities that you looked for when hiring that reflected those values? Often companies have a more structured set of core values that are baked into company events and communications. Honestly, at OkCupid, the people who worked there came from a certain place of idealism and community, so it just kind of sprung up. Everyone who worked there was encouraged to read feedback, so people would see all kinds of different perspectives from users using the site. When someone would read feedback and find an issue that resonated with them, they could bring it up, and we’d discuss it and think about how to best solve it for the people who sent in the feedback, but in a way that was respectful and helpful to the rest of the users on the site as well.
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Venture capitalists don’t just provide capital, in other words. They also have to raise it. A firm’s managers—called “general partners”—are responsible for finding limited partners to finance investments. They also often have to invest a meaningful amount of their personal wealth in the fund they work for, so that limited partners are assured they have skin in the game.
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Historically, limited partners have included major pension funds, endowments, and other large institutions. The city of San Francisco, for example, has $1 billion out of its $20 billion under management allocated to venture capital to fund the retirements of city workers. Even if these investment officers are leading mission-driven or civic institutions like philanthropic foundations or state retiree funds, they are legally bound to do what is in their client’s best financial interest—regardless of whether the side effects of generating those returns may conflict with the ostensible values of the institution’s beneficiaries. As global wealth inequality has deepened, venture firms are also increasingly raising funds from “family offices,” which manage the private wealth of the world’s richest families.
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Obviously sexual orientation and gender identity are not binary—they’re a continuum. But at first we simplified in terms of gay, straight, and bi orientation. And we were always thinking about the nine different pairings of those groups, and made sure any experience we created made sense for each of those nine different pairings.
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On the gender front, for a long time we were aware that people who didn’t identify as either male or female weren’t being completely served on the site, because there was no way for them to enter their identity—the site made you pick male or female. That was a tricky decision, because it was built into the code pretty deeply from the start. We really wanted to make that change, so finally we put in the time and effort and added a much better range of gender options. We were really happy we were able to do that, although it took a lot of work and took us a while to prioritize it.
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We looked around and discovered that there isn’t broad agreement on how you test a stethoscope because nobody really cares. We picked a method that uses a “chest phantom,” which is a simulation of a chest that’s a polyurethane balloon filled with two liters of water. You put sound in on one side and you collect it on the other side, with the understanding that there are a lot of reverberations in there that happen along the way. And then you compare the two stethoscopes to see how the sound is attenuated by the stethoscope. The stethoscope will take away some of the sound, but you want to make sure it’s not taking away sound in important places. So you record two audio files: one of the sound that travels through the traditional stethoscope, and one of the sound that travels through the 3D-printed stethoscope? Exactly. And then you run both files through a spectrum analyzer to see how they stack up. The spectrum analyzer we used was Audacity, which is open source. We used a Hello Kitty balloon, so colloquially we call it the Hello Kitty Protocol, but we couldn’t write that in a publication — we made it sound more scientific. The cost of new materials for the validation study is about $15, and then you also need the traditional stethoscope to validate against. You could say that’s a capital cost. You also need headphones with a microphone in them. And, of course, you need a Hello Kitty balloon filled with water. Beyond stethoscopes, what are the other kinds of devices you’re developing? Our big project that I’m really looking forward to is dialysis. Dialysis is also an interesting problem of capitalism. A good analogy is disposable razors. Broadly, in Canada, there’s Schick and there’s Gillette. You can’t use a Schick razor on a Gillette handle and vice versa. That’s called vendor lock-in. Fundamentally, dialysis machines are a pump, a controller, a flow meter, and a little bit of tubing. Nothing special. The only way for companies to make them profitable is to create vendor lock-in and collude with each other. In California, there was a ballot initiative [Ed.: Proposition 8] to figure out a way to reduce the costs of dialysis treatment. Fresenius and DaVita, the two biggest companies in dialysis, spent $111 million to stop the proposition and they got it killed. That’s good for them because they don’t want competition, and they don’t want price controls. That’s not how they make money.
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In Gaza, this creates a problem. Let’s say white people in the United States under Obama decide they want to donate to Gaza, which happened. They donate a bunch of dialysis machines and what are called “disposables.” Machines are about $35,000 a piece. A disposable is a filter plus a circuit of tubing that hooks into the patient to connect them to the machine. Together, the machine and the disposable do the work that a functioning kidney would do: they filter the patient’s blood. Each disposable is about $100 and should only be used once per session. It’s supposed to get thrown away. But in Gaza, patients take disposables home and wash them and bring them back the next time until they totally disintegrate — which is bad, bad news. It’s circulating your internal stuff. It should be sterile. Now let’s say that Trump is in office and the Americans have lost interest, but the French have decided that they like Palestine. So the French donate a bunch of disposables that don’t work with the American machines. We put away the American machines, retrain our nurses on French machines, and start using French disposables. It’s the same thing with the Spanish, the Russians, whoever. We have rooms of machines with no disposables, and rooms of disposables with no machines. Fucking crazy. So patients aren’t getting enough dialysis because we don’t have enough machines or disposables that work together. It’s like if a car manufacturer said you’re not allowed to use anything but Goodyear tires. The tire is the disposable, the car is the machine. This is what the companies are doing to us. They’re making it impossible for us to use anyone else’s disposables. How can 3D printing help? Our idea is to make a machine that’s generic, where we can create templates for each of the different companies’ disposables. The template would become an interface between our machine and the proprietary disposable, so that different disposables become compatible with our machine. It would consist of printed parts at every place where the disposable touches the machine, a sheet or a board with a cutout to hold each of those 3D-printed parts, and instructions for putting the disposable, template, and machine together.
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Above all, when I talk to Taiwanese people about what Taiwan did right, they talk about healthcare. In particular, they praise Taiwan’s single-payer healthcare system. Almost 99 percent of Taiwanese citizens and residents are covered by Taiwan’s national health insurance program. (The 1 percent, the government believes, consists of Taiwanese citizens residing outside the country.) When coverage hovered around 96 percent, the government made a concerted effort to track down the remaining 4 percent—composed primarily of Indigenous Taiwanese, the unemployed, the homeless, and orphaned children—to get them enrolled. Households below the poverty line receive free coverage. Essentially, no one is denied healthcare in Taiwan.
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With 99 percent of the population insured under one system, a centralized medical database made it possible for the government to rapidly implement its mask-rationing system. It also made performing contact tracing easier, as well as tracking community-based transmission. More importantly, universal health coverage means people aren’t afraid of being denied medical treatment or going bankrupt from medical bills. The government encourages citizens to report even mild symptoms, which enables the authorities to detect infection earlier. Ultimately, Taiwan’s success in containing COVID-19 has less to do with technology than with well-functioning state institutions that acted quickly and collectively. As a Taiwanese friend described it, the government’s approach has resembled crossing a river. You inch forward step by step, feeling your way across and making decisions as you go. Along with this experimental, adaptable spirit, the government’s focus on transparency and building public trust, paired with an excellent universal healthcare system, are the real strengths of the Taiwan model. Technology, while useful, cannot make up for the absence of strong public structures of care. In the United States, where the fight for universal healthcare is still an uphill battle, care is a luxury good with multiple prerequisites—employment, wealth, geography. In Taiwan, care is a basic human right that everyone receives equally.
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This could not only prevent the redundancy of small startups developing similar technologies behind closed doors, but also lower barriers to entry into the industry. It could facilitate cooperation with regulators, transparent scholarly analysis, and the establishment of industry standards, such as a moratorium on fetal bovine serum. Federal regulations and licensing agreements should require that cultured meat facilities are unionized workplaces and that qualified workers displaced from the conventional meat industry be given preference in hiring. The intellectual property developed this way would then, ideally, remain in the public trust and be farmed out to the private sector, which would commercialize a food product rather than patent food production.
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Most critical visions of cellular agriculture are dystopian: unaccountable corporate giants force-feeding a captive population with fake meat. Ironically, that describes the food system we already have. A world in which the factory-farmed nugget is replaced by the bioreactor-brewed nugget would be a monumental win for animals and the environment. If tied to progressive industrial and agricultural policy, it could also be a win for labor, public investment, land use, and champions of alternative foodways. Chicken nuggets might represent everything that’s wrong with our current food system, but cellular nuggets can help build a more sustainable future.
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Yup. Reminding people of their rights turns out to be one of the stock things the NLRB does in these kinds of settlements. We were really into it. We also received back pay for the period between our firing and the settlement, and we received a few weeks of additional pay that was similar to the severance agreements we turned down in order to be able to talk about what happened.
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As we wrap up, can you talk about where you're at politically now and how you see that as interwoven or not with your professional work as an open source developer? We are living through an extreme moment in history. The things that felt relevant even a couple of years ago don't have the same relevance or urgency now. I was focused last year on different kinds of ethics clauses in open source licenses. I’m not doing that right now. I've spent my summer watching people be beaten by the police. I’ve heard politicians say again and again that tear gas is bad, but then there is still tear gas. I’m one of many people who have been radicalized by this summer in Portland—by what the police and the mayor have done. I was already a lefty, but it's refocused things for me.
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When I have students who want to respond to me on Twitter, I now have to do a whole lot more teaching about how they can do that safely and responsibly—especially given the kind of stuff I teach about. I don’t want my students trying to debate something like race or gender in the public domain, because there’s a huge coordinated troll community looking for that kind of content so they can attack people.
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The way the names are collapsed in the tweet structure now makes it much harder for my students to figure out if they’re participating in a hashtag safely, and if they’re talking to who they mean to be talking to. Small changes like that are always happening, and it can change how you use Twitter pedagogically.
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How old were you at the time? Twenty-three. This was all very new to me. Even with some experience in the industry, I had no idea how corporations worked. No one in my family worked for a corporation. So we went into the bigger company and did a presentation for several directors. They found it interesting, because we were thinking about the same problems that they needed to be thinking about. Because the big companies missed the boat on mobile, they were willing to write checks to make up for that gap. That’s probably the biggest phase of talent acquisition that I’ve seen in my career. Although maybe today acquisitions around AI could rival it now.
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They liked our product enough to make an offer. But their offer was many multiples larger than the first one we’d received. They were a larger company, with more money to spend. They were also more frightened of smaller competitors outmaneuvering them, so they were willing to spend more. They wanted to acquire all of the assets of our company for a particular price and then wind the company down. The price they proposed was pretty high. And being a young kid with lots of student loan debt, I was blown away by the seriousness of that number. That’s the main thing I remember.
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I read that the Library Freedom Project's pilot project was setting up a Tor relay in a public library. What is a Tor relay? The Tor network is made up of nodes run by volunteers. When someone uses the Tor Browser from their own computer, their web traffic gets bounced to all those different volunteer nodes so that someone looking from outside can’t trace that person’s web traffic back to their computer at their house. A Tor relay is one of those nodes; it’s a computer that’s configured to just forward traffic all day to other nodes of the Tor network. We did set up a Tor relay in a library in New Hampshire around the summer of 2015. It wasn’t quite LFP’s pilot; I had already been running LFP for about a year at that point. But a lot of people first learned about us because the relay got the attention of the Department of Homeland Security (DHS), and that whole situation got some press coverage.
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How did the Tor relay project come about? And what was your pitch to that library? I had been traveling around the country doing these direct trainings, building relationships with librarians and helping them understand the surveillance problem from justice-focused angles. I was focusing on strategies that they could employ, both in their direct work with patrons, and then also in a bigger, advocacy sense—macro-level stuff they could do out in their communities. The Tor relay project came about because I started thinking about how libraries are in a strong position to run privacy infrastructure. And Tor relays were the perfect example. In order to run one of these things, all you need is a little bit of bandwidth. It also helps if you are an institution rather than an individual, because law enforcement might be more likely to hassle an individual, especially if they run one of the exit relays, which is identifiable on the network.
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Struggle on the Library Front What happens the day after the revolution? One answer is the reorganization of the library. In 1919, Lenin signed a resolution demanding that the People’s Commissariat of Enlightenment “immediately undertake the most energetic measures, firstly to centralize the library affairs of Russia, secondly to introduce the Swiss-American system.” Lenin presumably referred to the organization of the European libraries he had observed during his exile from Russia in the early 1900s. By imitating the “Swiss-American system,” the Bolshevik leader hoped to create a single state system of centralized control over the distribution of books and the development of collections.
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Four decades later, Cuban revolutionaries also recognized the importance of what Soviet leaders like Nadezhda Krupskaya had once called the struggle “on the library front.” In the aftermath of the Cuban Revolution in 1959, Fidel Castro appointed librarian María Teresa Freyre de Andrade as the new director of the Jose Martí National Library in Havana. A lesbian and long-time dissident who had been exiled and jailed by the previous regimes, she had long been concerned with the politics of librarianship. In the 1940s, she had articulated her vision of a biblioteca popular, a “popular library,” distinct from a merely “public” one. Whereas the public library may be a “rather passive” one where “the book stands still on its shelf waiting for the reader to come searching for it,” the popular library is “eminently active” as it “makes extensive use of propaganda and uses different procedures to mobilize the book and make it go in search of the reader.” After the revolution, Freyre de Andrade and her staff began to enact this vision. They brought books to the people by sending bibliobúses, buses that served as moving libraries, to rural areas where no libraries existed. They also began to develop a novel practice of revolutionary librarianship. Unlike with Lenin, the goal was not to imitate the organization of European libraries. In a 1964 speech, Freyre de Andrade argued that Cubans could not simply “copy what the English do in their libraries.” By doing so, “we would have a magnificent library, we would have it very well classified, we would provide a good service to many people, but we would not be taking an active part in what is the Revolution.” How could librarians take an active part in the revolution? One answer was to gather and index materials that had been excluded or suppressed from library collections in the pre-revolutionary period, such as the publications of the clandestine revolutionary press of the 1950s. But librarians also became involved in a broader revolutionary project: Cuba’s effort to build its own computing industry and information infrastructure. This project ultimately led to a distinctive new field of information science, which inherited the revolutionary ideals of Cuban librarianship.
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My work chat is a repeat of yesterday, gossip about whose heads are on the chopping block. Talk of a “rapid retreat” from upper echelons that none of us know how to interpret. I can’t vent to Aluna no matter how much I’d like to, not in front of the other refugees. She drifts back and forth, out into town, buying snacks for the children and a treat for the dog. This is the first time her offers are rejected. From afar, I see parents push her away. Gentle shakes of the head, hands rising up. She’s too far for me to hear how she responds.
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*** I suggest we leave at sunset. She doesn’t know how to take that. We’re exchanging messages over internal displays, eating the rations they’ve started handing out for lunch. Aluna would wait and see as she put it, staying in the shelter, till the next disaster forced us to relocate again. This is no home and yet she can barely imagine leaving it. Maybe it’s the proximity to what we lost that keeps her tethered. Cambridge’s parks, rowhouses, and stores provide a familiar urban texture, and when you drift far enough from the tents it succeeds in lulling you back to normality. It’s delusional. I work where the miracles are made, and the only thing Centra is concerned about is laying people off and minimizing damage for the PR fallout of the broken seawall—a tragedy that they refuse to claim as their responsibility, taking all distancing measures available. It will grow clearer over the coming days that there is no help on the way. Not for any of us. We have to leave the city.
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Thinking more broadly about this new kind of advertising, what do you think is most distinctive about it? How does it differ from what came before? One difference that springs to mind is the sheer individualization of it. There are some auctions where you can even bid for an individual human impression. For example, there’s a startup that will let you target a particular person with an ad campaign.
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How does that work? Maybe you want your partner to stop smoking. This startup will generate a special link for you that looks like it’s an e-commerce site. You send it to your partner and when they click it, they get a cookie secretly loaded into their browser. This cookie enables the company to track your partner across the web. You write up an anti-smoking ad, and the company will ensure that your partner sees that ad everywhere. Now your partner’s entire internet experience is permeated with pressures to stop smoking. You can design a similar campaign for a coworker you don't like. You can show them ads for job-hunting websites, to encourage them to get another job.
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In the modern history of systems of control deployed against subjugated populations, ranging from North American internment camps to the passbooks of apartheid-era South Africa, new technologies have been crucial. In China, that technological armament is now so vast that it has become difficult for observers to fully inventory. The web of surveillance in Xinjiang reaches from cameras on the wall, to the chips inside mobile devices, to Uyghurs’ very physiognomy. Face scanners and biometric checkpoints track their movements. Nanny apps record every bit that passes through their smartphones. Other programs automate the identification of Uyghur voice signatures, transcribe, and translate Uyghur spoken language, and scan digital communications, looking for suspect patterns of social relations, and flagging religious speech or a lack of fervor in using Mandarin. Deep-learning systems search in real time through video feeds capturing millions of faces, building an archive which can help identify suspicious behavior in order to predict who will become an “unsafe” actor. The predictions generated automatically by these “computer vision” technologies are triggered by dozens of actions, from dressing in an Islamic fashion to failing to attend or fully participate in nationalistic flag raising ceremonies. All of these systems are brought together in the IJOP, which is constantly learning from the behaviors of the Uyghurs it watches.
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The predictive algorithms that purport to keep Xinjiang safe by identifying terrorist threats feed on the biometric and behavioral data extracted from the bodies of Uyghurs. The power—and potential profitability—of these systems as tools of security and control derives from unfettered access to Uyghurs’ digital lives and physical movements. The justification of the war on terror thus offers companies a space in which to build, experiment with, and refine these systems. In her recent study on the rise of “surveillance capitalism,” the Harvard scholar Shoshana Zuboff notes that consumers are constantly off-gassing valuable data that can be captured by capital and turned into profitable predictions about our preferences and future behaviors. In the Uyghur region, this logic has been taken to an extreme: from the perspective of China’s security-industrial establishment, the principal purpose of Uyghur life is to generate data.
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AK: Understanding how technology intersects with the lives of patients and the broader culture is necessary extracurricular work for the psychotherapist. If I were to treat someone deeply embroiled in the world of Tinder, it would behoove me to at least have some idea of what Tinder is. More important would be the need for me to feel curious about what Tinder is to my patient, and not dismiss anything I don’t understand as corruptive or puerile, for to do so would be to dismiss a part of my patient’s life.
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My rule, if you can call it that, is that a patient is free to say whatever they like, however they like, and I am free to respond (or not respond) in whatever way I think will benefit the treatment. For instance, I will rarely respond to an email or text message from a patient other than to acknowledge its receipt, and perhaps suggest that we discuss its contents at the next session. Many things happen in the asymmetric, virtual space that defines modern communication—ranging from the wonderful to the horrific—but not, I think, psychotherapy. The patient has enlisted me to bear witness to her experience, which demands my presence and undivided attention.
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Digital technology has revolutionized this process. As mathematician Jordan Ellenberg explained in a New York Times op-ed, “Gerrymandering used to be an art, but advanced computation has made it a science.” While drawing districts for partisan gain is an ancient custom in American politics, computers have greatly improved its effectiveness.
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Digital technology has advanced to the point where carrying out a legal, effective, and durable gerrymander may hardly be more difficult that editing a picture in Photoshop. The consequences are troubling. Left unchecked, digital gerrymandering threatens to make competitive elections a thing of the past.
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I call that political common sense the “access doctrine.” The access doctrine decrees that the problem of poverty can be solved through the provision of new technologies and technical skills, giving those left out of the information economy the chance to catch up and compete. Like other forms of political common sense, the access doctrine mixes factual claims with ideological ones. It is clear, for example, that finding a job without an internet connection and a PC is difficult—just try filling out an application to work for CVS, let alone using USAJobs, on your phone. It is less clear that there are plenty of good tech jobs out there, just not enough coders to fill them. Economic reality is more complicated than political slogans might admit. Getting online and learning to code won’t change the rest of the labor market by itself. What’s more, the access doctrine implies a certain calculus for social worth: your social value diminishes alongside your economic value. Inequality is a feature of a capitalist economy, not a bug, and the access doctrine makes this inequality sensible and navigable. It explains why there is such a gulf between rich and poor, how the poor can find security, and what help they need to get there—all without disrupting the basic shape of these unequal social relations.
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Before Tomorrow Arrives The access doctrine is part of a wider story that explains the relative poverty or economic inactivity of specific populations, regions, and countries through the skills that individual workers possess. For much of the twentieth century, especially after the economic dislocations of the 1970s, representatives of business, education, and government have warned of impending or actual skills gaps, where the education system does not provide graduates with the skills businesses need, and the more specific problem of skills shortages, where US businesses cannot find a specific kind of worker—today, generally engineers and information technology professionals. Skill is notoriously difficult to define and measure. Nevertheless, the urgent problem of skills gaps and shortages has been given historical weight through the invocation, typically by economists, of skill-biased technological change (SBTC). Advocates for SBTC hold that the prevalence of technology—itself typically unmeasured and underdefined—increases in prevalence and complexity over time, and thus the demands for and wage returns of workers skilled in its design and use also rise over time. The vagueness of skill and technology are empirically troublesome but politically useful. A nebulous threat is always on the economic horizon, explaining that those struggling today do so because they have not sufficiently upgraded—and that they must do so before tomorrow arrives.
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But there’s reason to believe that this pushback has more to do with the interests of the telecom lobby than with good-faith concerns about the efficiency of Chattanooga’s experiment. With a $330 million price tag, the Gig was certainly expensive to build—but it has yielded significant returns on that investment. According to one study, the “smart grid” generates up to $67 million per year in combined revenues and savings. And its maintenance and operation are entirely funded by subscription fees, requiring no tax funding. The utility is solidly in the black, with a 57 percent market penetration that far surpasses initial targets and continues to grow.
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Rather than focus on the instructive lessons from success stories like Chattanooga, free-market critics prefer to frighten city governments with tales of municipal broadband gone awry, like the $39 million flop in Provo, Utah, which was bought by Google Fiber after years of mismanagement. But it’s hard to take this concern seriously when failed municipal projects are exactly what the private sector wants. The death of Provo’s municipal project simply became another investment opportunity for Google, while a success could have locked predatory telecoms out of the local market.
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JF: There’s a real pleasure in getting people together, feeding them pizza, and packing bubble envelopes. And always debating the most “efficient” way to do it. CH: Another thing that was surprisingly annoying for us was printing postage. At the very beginning we would take the packages to the post office at the old People’s Temple site in the Fillmore, which was terrible for our purposes. There was no parking outside. We had to temporarily park in a loading zone, and took incredibly heavy Ikea bags full of envelopes in, and then we printed the postage on site using the postage machine since we didn’t know how to do it at home.
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JF: We could only print like ten stamps at a time. There was a limit of how many stamps you could print at once, and total dollar limit of per card swipe, and we had to keep retyping the postage amount for Media Mail every time. That was truly a huge pain in the ass. CH: At some point multiple of our cards got marked for fraud while we were using the terminal and we couldn’t use them anymore. At that point it was like, well, shit, this is terrible and needs to change. We figured out how to pre-print postage from home, which was glorious, and started dropping them off at the post office in the Haight, which was smaller but easier to get to. As we started to scale–the people who worked at the post office were just so lovely, but also when they saw us coming in with boxes and boxes of envelopes, they were just like, please, no. They would open up the back and have us dump them directly into the mail carts. Then they were just like: please, arrange a pickup, don’t bring it here. We can’t fit it all on the truck. JF: We did try many times to order a pickup, and it never worked out.
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But a Ford Foundation vice president named Roger Kennedy took it upon himself to convince policymakers that it was “prudent” to invest not only in low-risk bonds, but in higher-risk equities. He wanted to create a standard of prudence that took into account the whole composition of the portfolio rather than individual investments. A Nobel Prize-winning economist and student of Milton Friedman named Harry Markowitz had developed a similar idea as part of “modern portfolio theory.” This would be a fundamental shift from a principle of risk avoidance to one of risk management.
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So Kennedy and his allies lobbied to make the prudent man rule more flexible. The political climate helped: the stagflation crisis of the 1970s, along with rising anxiety about emerging competitors like Japan, stoked fears among policymakers that the country would fall behind if it didn’t stimulate new business development.
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But every worker knows this is bad. Every engineer and designer knows this is awful. They’re not happy making these features. But they can’t argue with the data. The engineer and the designer who care about the user don’t want to put these features out in the world. But the data says those features are increasing time spent—which means they’re good. Because more time spent means selling more advertising, which means making more money.
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And so long as you’re working for an advertising company, what other metric besides time spent could there be? So long as you’re working for a company, what other metric besides profit could there be? That’s a similar question. You can make small surface-level improvements here and there. But you’re not going to tackle the core problem until you tackle the profit motive.
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And even though the software is built to get smarter and more accurate with machine learning techniques, the training data sets it uses are often composed of white faces. The code “learns” by looking at more white people—which doesn’t help it improve with a diverse array of races. Technology spaces aren’t exclusively white, however. Asians and South Asians tend to be well represented. But this may not widen the pool of diversity enough to fix the problem. Research in the field certainly suggests that the status quo simply isn’t working for all people of color—especially for groups that remain underrepresented in technology. According to a 2011 study by the National Institute of Standards and Technologies (NIST), facial recognition software is actually more accurate on Asian faces when it’s created by firms in Asian countries, suggesting that who makes the software strongly affects how it works.
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“These libraries are used in many of the products that you have, and if you’re an African-American person and you get in front of it, it won’t recognize your face,” said MIT Media Lab director Joichi Ito at the World Economic Forum in Davos at the beginning of 2017. As Ito points out, being invisible to a technology that can be used against you is extremely dangerous. It’s also a sad allegory for how black individuals are not seen in the criminal justice system. In a TEDx lecture, Buolamwini, who works with Ito and is black, recalled several moments throughout her career when facial recognition software didn’t notice her. “The demo worked on everybody until it got to me, and you can probably guess it. It couldn’t detect my face,” she said. “Given the wide range of skin-tone and facial features that can be considered African-American, more precise terminology and analysis is needed to determine the performance of existing facial detection systems,” Buolamwini told Recode in January.
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I wanted to start with your first day at camp at Standing Rock. In your book, you write about digging compost holes with an Ojibwe relative and building a kitchen shack with a Palestinian network admin. It seemed like an incredible logistical feat that brought together people from all over. Can you talk about the infrastructure you all built there and what made that convergence possible? My first day at camp was late August 2016, before the dog attacks. We arrived to bring supplies, and we set up camp for about a week. Some of us from our organization, The Red Nation, had to leave, but some of us stayed for a long time. One of our people stayed until the last day when camp was evicted in February 2017. By and large, the infrastructure of the camp was organized around tribal nations. Our tribe, the Kul Wicasa, or Lower Brule Sioux Tribe, set up our own camp. Next to us was the Ihanktonwan, or Yankton, and next to them was the Oglalas. Then there was the Cheyenne River Sioux camp and then across the Cannonball River, there was the Rosebud Sioux camp. The camp structure took on an organic shape. Later on, other organizations and tribal nations filled in. Because of the culture of Native people in general, our camping and outdoor life is really well organized. We have a depth of communal knowledge about those subjects. Even though we are colonized and confined to reservations and don’t live the life that we once lived, we still have a seasonal cycle of migration and gathering. Summers are very community-oriented and organized around a kind of camp life, whether it’s Powwows or fairs or Sun Dances or whatever. Then in the winter, we go back to our more settled homes. Camp life at Standing Rock reflected that.
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Everything was organized around need, so the first thing that went up were the porta potties. Then came the kitchens, followed by the donation tents where people could get camp supplies they didn’t have. It reflected the traditions of Indigenous people: if you didn’t have enough, you were still taken care of. Many people see Indigenous generosity as a weakness, but it’s one of our strengths.
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The internet lights up. Donegan gains tens of thousand of Twitter followers overnight. Weeks later, on Super Bowl Sunday, Harper’s publishes its anti #MeToo story online. The subhead announces that “Twitter feminism is bad for women.” www.harpers.org gets more clicks than it has had in ages. Over the next few weeks, Harper’s sees a modest bump in subscriptions.
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The process repeated itself the week after news of the Harper’s cover story leaked. On January 14, the online magazine Babe.net published an anonymized account of a bad date with the actor Aziz Ansari. Suddenly, a low-paid woman’s words (and screenshots of her text messages) went viral—and pundits at The Atlantic and The New York Times rushed to get a piece of the traffic by denouncing a feminist website that, until then, few of their readers had ever heard of.
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Which is a shame, because the movement had the potential to be so much more. Free software arose out of the desire to decommodify data, to contest the idea of treating information as property. Of course, the movement’s ability to fulfill this desire was hampered by a lack of political analysis and historical context. Crucially, free software advocates neglected to recognize information as simply the latest battlefield in a centuries-old story of capital accumulation, as capital discovers new engines of profit-making and new areas of our common life to enclose. Still, there was something there: glimmers of a recognition that property is the enemy of freedom.
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How to Set Software Free Recently, it feels like we’ve reached a turning point when it comes to public sentiment about tech. More and more people are questioning the power that technology companies have over our lives, our economies, and our democracies. Pundits and politicians are casting about frantically for solutions: better regulation; a code of ethics for engineers, more diverse workforces. Unfortunately, these are mostly window-dressing solutions that don’t address the imbalance of power at the root of the problem. But maybe a more radical approach was there all along, hiding in plain sight, within the history of tech itself.
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“That white spot,” Carmen says in her Spanish accent, throwing the car in park and twisting her body into the backseat to grab the strap of her professional-grade digital camera from where it sits next to me. “Bald eagle. Oh my oh my oh my.” Carmen had scoured the internet, trying to find out what was wrong with her—why was she suffering these inexplicable attacks of pain and irregular heartbeat that her doctors said were not a heart attack.
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“I first had like a stroke, my mouth couldn’t talk, it was like drunk, I couldn’t open the door to my car, my mind and my hand weren’t communicating.” She would have the attacks mostly at the middle school where she worked as a Spanish teacher. Sometimes she would have to call her husband to come pick her up. Soon the attacks came every week, then every day. She quit her job.
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Perhaps it was this theme that attracted a large group of tech entrepreneurs and investors from China. The media has reported that Larry Page, Jeff Bezos, Mark Zuckerberg, and Elon Musk have all been seen in the playa of the Burning Man Festival, and these figures are revered by Chinese tech entrepreneurs as the heroes of the present era. Some participants from prior years even attributed the success of their projects to the inspirational power of Burning Man. Admittedly, this neat combination of worshipping totems while pursuing practical benefits is quintessentially Chinese.
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This year, I also came here with a group of friends from all over the world and became a “virgin burner.” I had already learned about the so-called Ten Principles of Burning Man—but experiencing firsthand this miraculous feeling of order emerging from chaos proved to be remarkably different from the Chinese social experience of myriad rules and stringent controls. I had to spend several days slowly assimilating before I could savor the joy of this so-called “techno-hippie orgy.” I couldn’t help but feel curious about those Chinese entrepreneurs and investors who came in private jets from thousands of miles away. There was an entrepreneur training camp organized by the internet giant T———, and seventy startup owners were brought over by their investor, a leading Chinese venture capital company, M———. They hired a company to outsource their experience; this company set up expensive air-conditioned space-capsule tents and prepared large amounts of food, drinking water, and alcohol. One camp even had karaoke. But in the first four days, these luxuries, which were too high-end for traditional burners, sat untouched. Those Chinese guests only arrived, belatedly, on the fourth day. I heard that the most expensive slot for this camp cost $20,000, whereas a regular ticket for the Burning Man Festival cost only $425.
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In 2019, Berlin celebrated Equal Pay Day by offering women discounts on public transit. It provided these discounts automatically, by analyzing the faces of people purchasing tickets. On the face of it, as it were, this approach might appear innocuous (or even beneficial — a small offset to gendered pay disparities!). But in actual fact, the technology in question is incredibly dangerous.
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Automated Gender Recognition (AGR) isn’t something most people have heard of, but it’s remarkably common. A subsidiary technology to facial recognition, AGR attempts to infer the gender of the subject of a photo or video through machine learning. It’s integrated into the facial recognition services sold by big tech companies like Amazon and IBM, and has been used for academic research, access control to gendered facilities, and targeted advertising. It’s difficult to know all of the places where it’s currently deployed, but it’s a common feature of general facial recognition systems: anywhere you see facial recognition, AGR might well be present.
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Perhaps this is the most salient question when it comes to automated therapy: can the act of telling be separated from the act of listening? Does simply speaking create enough meaning on its own? If being listened to, recognized and understood by another human being is the source of therapy’s power, then there is no way bundles of code—such as ELIZA, Ellie, or the fictional app in the game I eventually created—could work. Automated mental health care would be built on a faulty premise about what therapy should be and what computers can ultimately do.
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Talking to the Dog Almost all of today’s mental health apps are built on that potentially flawed premise. Many take the form of an instant message–like interface with a friendly character, making comparisons to ELIZA difficult to avoid. One prominent example is Woebot, which boasts Stanford-linked founders and closed an $8 million funding round in 2018. As with its distant ancestor, Woebot’s lack of genuine human understanding is easily revealed. Telling the program I was feeling anxious about the fact that “I have so much work to do” resulted in this stilted interrogation, which reads as if it comes straight out of a CBT manual: Are you perhaps catastrophizing in your statement?NoAre you Future Predicting with your thought, “I have so much work to do”?NoYou’re doing awesome thinking through this by the way… we’re nearly thereAre you ‘Mind-reading’ in your thought, “I have so much work to do”?NoHow about black-or-white thinking in this thought: “I have so much work to do”?NopeGot itAnd is there a chance that you may be mistaking your feelings as evidence of the truth?NoOK Matthew, you did a great job there…Now for the most important bit… rewrite “I have so much work to do” without those distortions.I have so much work to doSo how did your rewrite go? The smarmy attitude and rigid dialogue of apps like Woebot makes one sympathetic to Weizenbam’s view that computerized therapy is inhumane. At the same time, I couldn’t help but notice again the similarity to my own work as a game developer. Using a bag of tricks not unlike the ones Weizenbaum used to create ELIZA, many games invite players to believe that digital characters are friends who can perceive and understand them in some way. Combinations of animations, sounds, and contextually appropriate behaviors create illusions not only of life, but of genuine communication between the alien worlds of humans and computers.
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It’s important that we don’t over-interpret these glimpses, though—we are so incredibly far from having a complete understanding of how any human experience plays out in the brain, let alone something as nuanced as romantic love. But we’re getting important clues: for instance, studies suggest that there can be similar patterns of brain activation between certain kinds of love and certain kinds of chemical addiction.
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In my work, I aim to place these insights into a philosophical context, so that while we marvel at love’s biology we don’t lose sight of the fact that it is also socially constructed. My dual-nature theory of love is designed to accommodate both at once. And, of course, the two interact. For example, the researchers wielding the tech—the ones designing our studies, and deciding what to look for in the first place—are themselves socially and culturally embedded creatures.
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Diogenes made a nice role model for Foucault and others. He was cool, aloof, and self-contained. He did what he wanted and didn’t owe anybody anything. He broke all the rules in public and lived a life of social transgression calculated to shame the powerful. He met his needs simply. When he wasn’t stark naked, you could imagine him looking good in a turtleneck, shades, and a shaved head. Alexander, the king of the world, admired Diogenes for his freedom. Alexander was free to do what he wanted within the limits of other people’s gold and cooperation—meaning that Alexander was profoundly dependent on cajoling, flattery and coercion. In other words, not very free. Embarrassing the Strong The kinds of provocations that Diogenes engaged in have long played a role in struggles of the weak against the strong. Jesus of Nazareth refused to answer his accusers and engaged in enough provocative public behavior, sarcastic metaphors, and contempt of ruling authority to invite some scholars to see him as a kind of Cynic. (His enduring image as a man with long hair, a beard, and flowing robes has the look of a Cynic.) Francis of Assisi once crashed his own birthday party disguised as a beggar to see how his professedly Christian friends would treat a stranger in need. The “Yankee Diogenes” Henry David Thoreau made a show of going to jail to protest US imperialism in Mexico and was annoyed when a well-meaning person paid the poll-tax to bail him out. Gandhi led his followers into confrontations where he knew they would be beaten up, and he made sure reporters were on hand to witness and publicize the abuse. Unions have practiced work-to-rule tactics to make corporate regulations look ridiculous by doing nothing not specified in the books. At Selma and elsewhere, African-Americans and their allies marched into the dogs, guns, and firehoses of Southern law. They provoked white supremacy to show its ugly face. When Martin Luther King Jr, who learned a lot about provocative dialogues from Thoreau and Gandhi, was jailed in Birmingham, the first thing he wanted to know was whether the protests had been covered in the national news.
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In this light, the Cynical tradition looks like a formidable tool against injustice. The core Cynical tactic is the delegitimization of power through public embarrassment. If the powerful show themselves incapable of embarrassment, that has usually only served as further proof of their moral failing. But Cynical provocation, I fear, has been hijacked. Today, the tactics that Diogenes, Gandhi, and King used have been taken over by trolls. Triggering the Libtards The alt-right has stolen a page from Diogenes’ book. The Essential TRS Troll Guide is written in a bro-ish slang that treats driving “senile old liberal cat ladies into apoplexy” as a kind of massive multiplayer video game; it directs the would-be provocateur not to show emotion, not to rise to the bait. The aim is to goad someone else into getting upset, an act known as “triggering.” In a comic-vulgar kind of sociology of the internet, the guide anatomizes different types of targets and notes the forums that offer a “target-rich environment” of “libtards.” The troll, like a drone operator, does his damage behind the safety of the screen. They get hot, but he stays cool. The first and most obvious context for the hijacking is technological. It is so much easier to see and share the comic and exasperated results of goading when everyone is armed with a video camera. Gandhi and King relied on sympathetic reporters and editors who worked at trusted news agencies; trolls rely on viral video to stir outrage and lulz and have little interest in context, explanation, or getting their story into establishment media. The Cynical dialogue of provocation is in shambles today in part because it has become so promiscuous in its creation, editing, and dissemination. But the proliferation of digital outlets does not explain the whole story. Alt-right cynicism is unhinged from an interactive relationship with the target. Diogenes, Gandhi, and King put their bodies on the line: they interacted with their adversaries in the dangerous meatspace of physical presence where face, name, person, and even address are mutually known. You don’t need to doxx someone you are talking to in the flesh.
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As always, the play is playing with perception. Can a person ever know what’s real? Is that a reasonable thing to even care about? The point is also that Polonius cannot be trusted; he is a sycophant. Hamlet will stab him dead two scenes later. 2/ Clouds are ambiguous. Liquid solid. Ethereal material. As Shakespeare’s Mark Antony says, their most striking images “mock our eyes with air.” The writers in this issue think about clouds of various shapes. Several address the cloud—that is, the global archipelago of warehouses that collectively coordinate the world’s computing power. Today, the press tends to talk about the cloud in imperial terms. It is the Valhalla of Amazon, Microsoft, Google, Alibaba; it is strongman leaders demanding data sovereignty for their country of a billion plus people. But those who have worked in data centers for decades tell a different story. The cloud, they point out, came into many firms from the bottom up, at the behest of engineers, not management. Yet even those who were there on the ground floor, who worked with “bare metal” and knew what it felt like to cut your fingers on the rails that held racks of servers in place (“a badge of honor”), could not anticipate the new forms of power that the cloud would bring. The advent of the cloud did not only create the conditions for the concentration of unprecedented amounts of wealth and information in the hands of a few firms. It also changed how rank-and-file engineers worked. The so-called Agile revolution started before the cloud took off. But it gained speed with it. Like previous developments in the computing industry, Agile combined counterculture and cyberculture; it was ostensibly rebellious, but committed rebels to sprinting toward corporate goals. Other writers in this issue take, literally, to the sky. Aloft, clouds remain difficult to assess. Even as political consensus in favor of trying to reach “net zero” grows, climate scientists will struggle to measure emissions and to compute what it would take to offset them. One trick of the cloud metaphor is to suggest that recording and computation are everywhere, and yet hazy points remain.
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Obscurities remain on clear days. Taking sprawling aerial surveillance programs in their sights, other contributors argue that the obscurity of individuals and organizations who have amassed the power to see everything must be dispelled. They share strategies for gaining information about government and corporate plans. Transparency is always a struggle.
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But in order to sort through the arguments surrounding AB5 and grasp the significance of this moment, we must do something that the discourse around ride-hailing has failed to do: situate ourselves historically, tracing both the continuities and the discontinuities that the cabbie Mark pointed to. Our present moment is largely the product of two neoliberal shifts in the taxicab industry—and, in a certain sense, in US society as a whole—that occurred in the late 1970s and the 2010s. Understanding the reasons for these shifts can help us get beyond the easy assumptions made on different sides of the debate: that employee status is an unalloyed good or ill, that innovation made the rise of Uber and Lyft inevitable, or that the issues raised by the sector are matters of technology rather than politics.
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Few people understand those reasons better than the drivers themselves—though, like other workers, they rarely have their voices centered in public discourse. By listening to drivers’ accounts of how their industry operates and has changed, we can come to understand how and why, despite some fears and ambivalence, they are using employee status to create a much-needed friction in the wheels of technocapital.
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Community Infrastructure Elsewhere, the possibilities of the utility sparked a more revolutionary aspiration: to create information services that supported communities and social movements, rather than businesses and markets. This was the dream at ONE, an experimental urban community founded in 1970 in San Francisco. Its members were artists and technical workers; largely college-educated, white, and in their late twenties. As an early prospectus for the community noted, their generation had come of age at a time when the demand for skilled labor seemed infinite. They emphasized the “utilization of industrial surplus,” a surplus that Cold War America was producing a lot of: armies of technical workers and warehouses full of computing machines.
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ONE happily gathered both in support of their fledgling community. Among the haul: a surplus XDS-940 mainframe from the San Francisco-based insurance conglomerate Trans-America. The XDS was tailor-made for utility computing—for being shared among users through time-sharing. Soon, the communalists of ONE put the machine to work on a number of projects, including a system called Community Memory. Coming online in August 1973, Community Memory functioned as a digital bulletin board for the Bay Area. It consisted of a teletype terminal on the third floor of Leopold’s Records in Berkeley that anyone could come in and use. The machine used a modem to connect to the XDS over a phone line, and users could enter commands to add or retrieve listings: “ADD” to contribute text, or “FIND” to search for it. Responses would then come back over the phone line and get printed out on the teletype printer.
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Work, we are often told, is becoming insecure. In reality, insecurity precedes work, or at least its waged variety. While some claim that insecurity is the inevitable consequence of innovation—the result of the fact that, as labor productivity rises, you need fewer workers to produce the same output—the fact is that people had to be made insecure, literally severed from their land and livelihoods, for capitalist working conditions to be foisted upon them. Before the wage-earner could emerge as our society’s paradigmatic subject, the persona that we must all embody to survive, the condition of what historian Michael Denning calls “wagelessness” had to be imposed via the process of enclosure, after which peasants could no longer provide for themselves. “Capitalism begins not with the offer of work, but with the imperative to earn a living,” Denning writes. Contrary to the myth of liberal laissez-faire, employment relations are anything but natural, spontaneous, or freely chosen.
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It wasn’t until during the New Deal era that employment became secure, at least for a subset of white men. During the Great Depression, an unlikely assortment of social reformers, radical workers, and “welfare populists” pushed to redefine “security” as a social good guaranteed by the government. "For a long time now people have been saying that perhaps the greatest evil of capitalist industrialism is not its unequal distribution of wealth but the insecurity it brings to the majority of the population,” The New Republic opined in 1935. If capitalism was the problem, the Roosevelt Administration’s solution was a robust welfare state. “Security” became FDR’s rallying cry.
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No More Ghosts On its own, Benjamin’s notion of a “normal life” would have been nothing but laughable — and laugh is what most of my friends do when I point them to the bit where he doesn’t think queer trans people exist. But because of how widely his instrument of measurement has worked its way into systems of power, it has been deeply influential. Benjamin’s textbook and, more importantly, his scale, became standard in trans medicine, informing the design of the Diagnostic and Statistical Manual of Mental Disorders (DSM) definitions of gender dysphoria and the rules of the World Professional Association on Transgender Health (WPATH) — considered (by doctors) to be the gold standard in treatment approaches. Those rules still contain a “real life” test and psychiatric gatekeeping, and the DSM only began recognizing non-binary genders as real in 2013. More broadly, public narratives of transness still tell “the story” popularized and validated by Benjamin — the trans woman “born a girl, seeing herself in dresses,” the trans man who has “always known” — even when that story does not and has never represented many of us.
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The consequences for people who do not conform have been dire. People are denied access to medical care for not meeting the formal medical definition of a “true transsexual”; people are denied legitimacy in trans spaces for not “really” being trans; people are convinced by these discourses that their misery must be fake — that because they don’t fit a particular normative idea of what a trans person is, they’re not really a trans person at all, and so should go back into the closet for years or decades or the rest of their lives. All because of a tool that claimed merely to measure gender. Inside and outside our communities and selves, Benjamin’s ghost continues to wreak unholy havoc.
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Atoms into Commodities The creators of these new platforms believe that they can solve the knowledge problem of net zero with software. Given the proliferation of net-zero pledges, this is a profitable problem to solve. But the platforms all approach this problem in a particular way: they turn carbon into a tradable commodity. This points to a broader point: net zero, in its current configuration, is a market-based project. It requires creating a global market where offsets can be freely bought and sold. This market already exists, but it has much room to grow; Mark Carney, the former governor of the Bank of England, says it could be worth $100 billion. Creating that value, though, hinges on turning lively carbon atoms into a smooth commodity. And that task, in turn, hinges on code. The new platforms aim to improve and expand carbon markets by packaging carbon into a reliable product that can be easily bought and sold online. Their value proposition isn’t just about using digital tools to do superior carbon monitoring and accounting, but about disrupting traditional carbon markets by disintermediating them.
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In traditional carbon markets, supply and demand is linked by retail traders who purchase carbon credits from suppliers and bundle them into portfolios, to be sold on to brokers or end buyers. This is an inefficient system, with too many middlemen; it is also rife with fraud. The new platforms want to cut the knot by connecting buyers and sellers—that is, the producers of positive emissions with the producers of negative emissions. Think of Veritree: corporations can purchase offsets by directly sponsoring planting projects in the developing world, without having to navigate a tangle of traders and brokers.
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Right. Again, context matters. Another good example is the online fallout after the European football championship. Here in the UK, our team played Italy in the final. It was pretty monumental, because we haven’t been in the final for fifty-five years. The game was close. It went to a penalty shootout at the end, and the three players who missed the penalty shots were Black. The UK lost. You can only imagine the extent of racism directed at these players afterward. It was horrific. On social media, racist posts often used monkey emojis to refer to the Black players. So people began calling for social media companies to take action. Some folks asked, if you can slap a warning label on every post about Covid, why can’t you slap a warning label on every post that uses the monkey emoji to be racist? The problem is that context is everything. The same emoji or word can be racist in one context but then in another context might be a vernacular within a community. That’s why we’re always going to need humans to do content moderation. There was an interesting post on Twitter recently from a former content moderator. They were talking about how there were so few pathways for promotion and progression. Moreover, moderators at large social media companies often get no say on policy, despite the fact that they’re the ones doing the work. So yes, we need to improve their working conditions, and we need to find automated ways of taking the most traumatic content away from them. But we should also be transforming their very job description. They should become specialists in particular subject areas, so that they can better recognize context and better interpret nuanced content.
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What else do you think should be done? We need transparency. But we also need to be specific about the kind of transparency we’re asking for, instead of just saying to these companies, “Be transparent.” This is something I’ve learned from Nic Suzor’s work, in particular. I’ve been wrong about this issue in the past. A few years ago, I said platforms needed to publish lists of banned hashtags, because there’s a lot of discrimination present in the hashtags they ban. But often when you ban a hashtag, people just move the conversation to a different hashtag. So publishing a list of banned hashtags can make it easier for people to come up with workarounds. That’s one of the many reasons why we need to be careful with what we’re asking for when it comes to transparency. One form of transparency we really need is around content moderation guidebooks. In my view, we need to see most, if not all of the rules that content moderators are using to make decisions. It troubles me that these are hidden from the public, and therefore hidden from scrutiny. And maybe I’m being too idealistic here, but I believe it would make a big difference if researchers had access to those rules and could make evidence-based recommendations for their improvement. You’ve written on how feminist thought can inform our approach to content moderation, and to young people’s mental health on the internet more broadly. What in particular do you draw from the feminist tradition, and how does it bear on the question of where we should go from here? One of my biggest influences both in academia and in life is Dr. Carolina Are. She’s an academic, activist, and pole dance instructor, and often posts images and videos of her pole dance tutorials on social media. Carolina is constantly having her account suspended, then reinstated, then suspended, then reinstated. She gets told that she’s broken the guidelines and then, a day later, gets told it was a mistake. The reality is that social media companies often don’t know where they stand on issues like female nudity. That’s why they’re so inconsistent. What they want to do is to come up with one global rule. They want to have a single guideline about female nudity that they can globalize across the entire platform. But female nudity is an issue that is viewed so differently according to the country that you’re in, the region of the country that you’re in, the religion that you belong to. Many different elements factor into it. So, to have one international rule on an issue like that is impossible. On certain things, generalizability isn’t possible.
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Eating Reality At one level, then, big data is about literal bigness: the datasets are larger and more diverse because they are drawn from so many different sources. But big data also means that data can be made more meaningful—it can yield valuable lessons about how people or processes behave, and how they’re likely to behave in the future.
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This is true for a few reasons. It’s partly because we have more data, partly because we have faster computers, and partly because developments in fields like machine learning have given us better tools for analysis. But the bottom line is that big data is driving the digitization of everything because any scrap of information, when combined with many other scraps and interpreted en masse, may reveal actionable knowledge about the world. It might teach a manufacturer how to make a factory more efficient, or an advertiser what kind of stuff you might buy, or a self-driving car how to drive.
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If what we encounter on Facebook, OkCupid, and other online platforms is generally “safe for work,” it is not because algorithms have sorted through the mess and hid some of it from view. Rather, we take non-nauseating dips in the digital stream thanks to the labor of real-live human beings who sit before their own screens day and night, tagging content as vulgar, violent, and offensive. According to Chen, more people work in the shadow mines of content moderation than are officially employed by Facebook or Google. Fauxtomatons make the internet a habitable place, cleaning virtual public squares of the sort of trash that would chase most of us offline and into the relative safety of face-to-face interaction.
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Today many, though not all, of the people employed as content moderators live abroad, in places like the Philippines or India, where wages are comparatively low. The darkest tasks that sustain our digital world are outsourced to poor people living in poorer nations, from the environmentally destructive mining of precious minerals and the disposal of toxic electronic waste to the psychologically damaging effects of content moderation. As with all labor relations, race, gender, and geography play a role, determining which workers receive fair compensation for their labor or are even deemed real workers worthy of a wage at all. Automation, whether real or fake, hasn’t undone these disturbing dynamics, and may well intensify them.
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Hello, Interoperator? When you type a message into your smartphone or scroll through the articles on your Facebook feed, you are interacting with the work of the Unicode Consortium. The consortium is responsible for encoding all the characters you see on your screen—letters and numbers in various scripts, hanzi and kanji, dingbats—into binary, so that they can be read on pretty much any machine, with any operating system, anywhere in the world. The purpose is to ensure that a line of code written in Bangalore, or a tweet fired off from San Francisco, arrives at its destinations essentially unchanged. Want a functioning Bengali website that can be accessed just as easily in East London as in Dhaka, or a Chinese-manufactured tablet that can render fonts designed in Accra? You need Unicode for that.
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By encoding many of the world’s scripts—Latin, Arabic, Greek, Tamil—in this way, the Unicode Consortium plays a crucial role in determining who can use the internet, which languages will survive digitization, and who can reap the gains of the digital age. It also helps to determine who can enter the global digital marketplace as consumers, advertising targets, and data sources for extractive surveillance capitalism. In other words, universal standards for interoperability are not just about bringing people online and connecting them—they’re also about driving profits through an ever-expanding digital ecosystem.
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But I found it technically challenging to lurk effectively, because the communities are so steeped in their own impenetrable lingo. Nduon, the psychiatrist, tells me that the conversations in her pregnancy subreddit can be difficult for outsiders to parse. “It’s very hard to understand what on earth people are talking about if you’re not familiar with the medications or different procedures.” She contends that it’s not just a pregnancy thing, but a human thing: “You create a language whenever you join a new group. You fall into the language that the group uses; it makes you feel like a part of the community.” The lexicon not unites the in-members, but also effectively shields them from intruders. The amateur scientists who can rattle off their hormone levels and treatment protocols are bound not just by their common language, but by the many ways in which modern medicine has failed them. These women have sought out the top doctors and the soundest science, scouring the research and polling fellow lay-reproductive endocrinologists to maximize their chances of conceiving or giving birth to a healthy baby. But for all the promises of cutting-edge technology, the common denominator for women heading down that road is past disappointment. When faith in the latest innovation falters, a trustier form of tech is waiting in the wings. And an older form of tech: old-school message boards that promise anonymity rather than the interconnectedness of social media. The web 1.0 setup of these forums is a feature, not a bug: that wild and wooly quality of the early internet makes room for a rare kind of openness and honesty among intimate groups of strangers. Keeping it Sticky in the Cyber Sweatshop Long before branded content was a twinkle in any media corporation’s eye, early internet companies recognized the value of building communities that produced the “‘stickiness’ that maintains users’ attention and increases the emotional cost of shifting sites,” in the words of feminist theorist Kyle Jarrett. According to a 1999 Wired article, AOL deployed (not employed) tens of thousands of “community leaders” to keep its chat rooms and message boards humming, compensating them in the form of free AOL memberships and select online perks — that is, until a group of them brought a class-action lawsuit against AOL’s “cyber sweatshop.”
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Two decades later, women are still providing the same free labor, keeping the leading pregnancy sites nice and sticky. If they feel like a vestige of the past, it’s likely because there’s not a lot of incentive for their owners to update them. “They’re still getting a ton of content, and that content is coming up in searches on Google, so as it is it’s probably drawing a lot of traffic without them having to invest anything,” Wexler, the bioethics fellow, explains. When I reached out to one such site, The Bump, to find out more about its community, a representative was keen to steer me toward their social media content instead. She explained that while their forums “originally served our users by fostering a sense of community for new and expectant parents,” they have “taken note of the shift away from forums and towards social media” and shifted their own attention accordingly. I had a hard time squaring this supposed migration with the numbers: The Bump’s Facebook page has fewer than 300,000 followers, while over at the message boards, the “Trying To Get Pregnant” section alone has 223,500 discussions and nearly three million comments. A single thread titled “what does a positive pregnancy test really look like??” has over 500,000 views. A quick scroll through The Bump’s Facebook feed may help explain why. It shows plenty of upbeat material and clickbait-y headlines, with the occasional frazzled mom or baby with spaghetti on his head as the only nod to the tougher side of pregnancy and motherhood. But if you’re panicking over bad test results or low hormone levels, the knowledge, support, and advice of like-minded women is a lot more useful — and comforting — than a funny #momfail. As I faced down the prospect of a terrifying diagnosis or miscarrying altogether, the sanctioned kinds of “problems” discussed in the official content on sites like The Bump — morning sickness, body image issues, and worst of all, “baby brain” — began to enrage me more and more. It was as if the worst thing that could happen to you was throwing up at your desk.
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I joined CORFO as an operations research scientist. We were extremely young, and this was an extraordinary opportunity to do things that people who were much older than us would find difficult. Fernando and I started to talk about how to proceed with the policies of Allende's government, which included a plan to expand the nationalization of Chile's industries with the aim of creating a strategy of economic development that prioritized the production of affordable goods. We wanted to create simple, accessible, and efficient industries that would produce products that everyone could afford.
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One way to do that was to centralize the economy. Now, Fernando and I didn’t want centralized planning. But, for at least the first year and a half of Allende's government, that's what CORFO was doing. Still, we thought there were more imaginative ways to bring together government policy with current developments in knowledge. It was in this period that we started to work with Stafford Beer, a British management consultant known for his work on cybernetics. Through Beer, we found how to achieve decentralization through devolving power. We needed to create the conditions where people could express their wishes and interests.
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MW: AI is an umbrella marketing term. It’s not a term of art that describes a specific technique. Companies apply the name AI to data-centric approaches generally, and you never quite know what you’re buying if you’re licensing an “AI” system. The AI boom of the last decade was not the result of a major scientific innovation in algorithmic techniques. It was a recognition that with massive amounts of data and computing power, you can make old techniques do things they couldn’t do before. The ascent of AI was predicated on concentrated tech company power and resources which had, as their driving force, the surveillance business model. One thing we rarely discuss is how AI research and development’s dependence on corporate resources worked—and continues to work—to shape and in some cases co-opt knowledge production. In other words, to “do AI” as defined in the current “bigger is better” paradigm, you increasingly need resources that are controlled by these handful of companies. You need access to really expensive cloud compute, you need access to data that is hard and sometimes impossible to get. You can’t just go to the data market and buy it—you often need to get access from the data’s creators or collectors, who are often the tech companies. It’s fair to say that academic computer science disciplines underwent a kind of soft-capture, in which as a condition of doing “cutting edge” AI research, over the last decade they became increasingly dependent on corporate resources, and corporate largesse. This dynamic led to practices like dual affiliation, where professors work at a tech company but have a professorial title and produce research under their university affiliation. It’s led to tech companies moving whole corporate labs into the middle of universities—like Amazon’s machine vision lab at Caltech. We have a structural imbrication between a massive, consolidated industry and knowledge production about what that industry does. And this compromised entanglement has bled into the fairness and ethics space, in many cases without anyone commenting on it. There are many forces working against our recognition of how captured the technical disciplines are at this time, and how easy it is for them to extend this capture into fairness, ethics, and other disciplinary pursuits focused on the consequences and politics of tech.
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To pick one example, Amazon is underwriting half of the National Science Foundation’s Fairness in Artificial Intelligence grants. And while a few people called this out, the fields concerned went on to apply for this funding, and uncritically applauded colleagues who received it. Whole labs are reliant on Amazon, Google, Facebook, Microsoft funding, and if you raise questions about it you’re endangering your ability to support your postdocs, your ability to obtain future funding, your standing with your dean. Or, you’re endangering your colleagues in these same ways. Dissecting the particularities of what it means to be able to do research on AI and related technologies, and how dependent this work often is on corporate resources, is a project that I think can help develop a clearer political-economic read of tech and the tech industry overall, and reveal the capital interests that are propelling research and knowledge production into tech and its implications. SN: This is a critical area especially during the time of Covid-19, when we saw how fragile so many of our public institutions are. We really feel that at a place like UCLA, where teaching assistants aren’t paid adequately, it’s extremely expensive to get an undergraduate degree, and the pressures to deliver public education are intense. Many, many systems are broken, and it is very painful to work under those kinds of broken systems. Meredith, I recognize this tech sector political economy you’re describing. They are capturing not only scholars but policymakers who, in essence, use public money to subsidize the entire industry, both through the research efforts at the National Science Foundation and also by making it impossible for democratic public institutions to flourish, because they don’t pay their fair share. They offshore their profits, and they don’t reinvest them back into communities where they do business in extremely exploitative ways. They just expect the public to underwrite it through tax refunds. How in the world can companies like Apple get tax refunds except through pure corruption? As we struggle in our communities with and in our institutions, we have to identify why those conditions are present. We have to recognize who has monopolized all of the resources and we have to examine the narrative about what’s happening with those resources.
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If you’re interested in donating to our efforts to create the first ever cooperatively owned adult fan site, please sign up for our newsletter at peep.me. Since the early months of the Covid-19 pandemic, María’s house has been run like a factory. Every day, her family of six synchronizes their routines so two people are always behind a computer. María, her husband Rodrigo, and their children, Daniela (20), Andrés (18), and Camila (13) are among the unknown number of Venezuelans who, after years of political and economic crisis exacerbated by the pandemic, now try to make a living by annotating data through crowdsourcing platforms. Using two Canaima laptops, which the Hugo Chavéz government provided a decade ago for school children, they tag images and videos, transcribe text and audio, search for information online, and send videos and pictures of themselves to developers at companies and research institutions in Europe and North America. The developers use this data to train machine learning algorithms, like the ones that do facial recognition, moderate content, and guide self-driving cars.
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The family’s activities all revolve around data production because this is their only source of income and, according to María, they have to “focus on the same objective to survive.” She and Rodrigo do most of the work, although she also takes care of many domestic duties. Camila, Andrés, and Daniela work part time on data annotation while attending high school and university. Only María’s youngest child, Sebastián (7) is able to focus exclusively on school. Although most crowdsourcing platforms’ terms of use state that each account must be run by a single adult, often the only hard requirement to set up an account is for someone to prove that they are at least eighteen years old by taking pictures of an identification card and their face, and praying that a third-party facial recognition verification system called Onfido detects a match.
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We started chatting and arranged to meet for drinks. “Meeting for drinks” is synonym for “you know what happens after a few drinks.” But when I told my friend about this economist who shared my enthusiasm for teas, she immediately asked whether his name was Graham. “Well, I know very few Western academics in Hong Kong who are single and knowledgeable about tea,” she said. It turns out that Graham is a colleague of hers, and they had just “met for drinks” recently, and it had indeed led to physical intimacy. I was both surprised and sorry. “I am so sorry. I had no idea.” My friend smiled. “Don’t worry about it. This is Hong Kong.” A Point in Between Hong Kong has long been known for its transience. People who come don’t come to stay. As a financial center without political autonomy, it is a capitalist utopia for the global super-elite, full of opportunities for those who have no intention of settling. This quality of Hong Kong once prompted the literary scholar Ackbar Abbas to assert that the city has always been, and will perhaps always be, a port in the most literal sense—a doorway, a point in between. Hence it makes little sense for transients to invest anything major and permanent, emotions included.
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Against this backdrop, romance has always been strange. This strangeness is further amplified by factors such as a falling marriage rate due to high housing prices and long working hours, and a sense of uncertainty about the city’s future. Since the handover from British to Chinese rule in 1997, Hong Kong has been governed under the “one country, two systems” principle enshrined by Article 5 of the Basic Law, the city’s mini-constitution. While this arrangement isn’t set to expire until 2047, growing pressure from Beijing in recent years has constrained the city’s autonomy. In 2014, a set of reforms that gave Beijing greater control over local elections sparked the Umbrella Revolution, a massive street protest that shut down core parts of the city for more than a month. Beijing’s influence has also shrunk the space for women’s self-determination. This space was never enormous: the patriarchal traditions of southern China, with their emphasis on family piety and male dominance, have long dominated Hong Kong. In the colonial era, these traditions were often embraced by Hongkongers as a form of opposition to British rule, and their power continued uninterrupted in the twentieth century. By contrast, in mainland China a wave of unrest during the 1910s and 1920s—the New Culture Movement, the May Fourth Movement—culminating in the Communist revolution of 1949 weakened the grip of old Confucian customs. The result is that traditional patriarchy is much better preserved in Hong Kong than in major mainland cities like Shanghai.
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Fortunately, there is an alternative. You can see the beginnings of it in commons-based movements like open access journals, Creative Commons licensing, and open-source software, which Wark calls the “gift economy.” These movements have flourished partly because they correlate with the common-sense idea that “information wants to be free,” and despite their limitations, they still hint at a deeper revelation. As Wark writes: …the vectoralist class quite rightly sees in the gift a challenge not just to its profits but to its very existence. The gift economy is the virtual proof for the parasitic and superfluous nature of vectoralists as a class.
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Seen in that vein, the radical undertones of open source didn’t just come out of nowhere, and they’re not unique to software. Instead, open source is simply a response to the very real contradictions that abound when property rights are applied to information. Where it fails is by offering an easy way out—by creating a microcosm, itself commodified, that suspends intellectual property conventions on a small scale, without ever presenting a viable alternative to the wider intellectual property regime required under capitalism.
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This issue becomes especially pressing when one considers that contemporary software is likely to involve things like machine learning, large datasets, or artificial intelligence—technologies that have shown themselves to be potentially destructive, particularly for minoritized people. The digital theorist Ian Bogost argues that this move-fast-and-break-things approach is precisely why software developers should stop calling themselves “engineers”: engineering, he points out, is a set of disciplines with codes of ethics and recognized commitments to civil society. Agile promises no such loyalty, except to the product under construction.
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Agile is good at compartmentalizing features, neatly packaging them into sprints and deliverables. Really, that’s a tendency of software engineering at large—modularity, or “information hiding,” is a critical way for humans to manage systems that are too complex for any one person to grasp. But by turning features into “user stories” on a whiteboard, Agile has the potential to create what Yvonne Lam calls a “chain of deniability”: an assembly line in which no one, at any point, takes full responsibility for what the team has created. The Agile Manifesto paints an alluring picture of workplace democracy. The problem is, it’s almost always implemented in workplaces devoted to the bottom line, not to workers’ well-being. Sometimes those priorities align; the manifesto makes a strong case that businesses’ products can be strengthened by worker autonomy. But they’re just as likely to conflict, as when a project manager is caught between a promise to a client and the developers’ own priorities. “There’s a desire to use process as a way to manage ambiguity you can’t control,” said María Matienzo, a software engineer for an academic institution. “Especially in places where you’re seen as being somewhat powerless, whether that’s to the whims of upper management or administration. So you may not be able to influence the strategic direction of a project at a high level, but Agile allows that certain conception of developer free will.” The product manager I spoke to put it more bluntly: “Agile tricks people into thinking they have ownership over their work, but from a labor perspective, they literally do not have ownership, unless they have, like, significant stock options or whatever.” Software development has never fit neatly into the timelines and metrics to which companies aspire. The sheer complexity of a modern application makes its development sometimes feel as much alchemical as logical. Computers may have emerged as military equipment, but completely subordinating programming work to the priorities of capital has been surprisingly difficult. When software engineering failed to discipline the unwieldiness of development, businesses turned to Agile, which married the autonomy that developers demanded with a single-minded focus on an organization’s goals. That autonomy is limited, however, as developers are increasingly pointing out. When applied in a corporate context, the methods and values that Agile esteems are invariably oriented to the imperatives of the corporation. No matter how flexible the workplace or how casual the meetings, the bottom line has to be the organization’s profits.
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By utilizing just a small portion of this wasted talent, Shirley rescued many women’s skills from being discarded entirely and helped British industry and government fulfill some of the promise of computerization. But for every woman Shirley employed there were always several more applicants she could not. The massive waste of human talent rippled upward, eventually destroying the British lead in computing and the British computer industry.
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In computing, discrimination is as old as the field itself. And discrimination has shaped the field in ways we are only now coming to understand and admit. The technical labor shortage in the UK was produced by sexism—it did not represent a natural evolution of the field, nor a reflection of women’s talents, goals, or interests.
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I have come to realize that it is not the wisdom of leaders, but the contributions of the many “ordinary” feminists that keep the feminist movement alive. The rank and file contribute a large amount of unpaid work, and broadcast the work of Feminist Voices by relaying articles and working around censorship. It is through them that I had realized more deeply than ever that Feminist Voices was so important to everyone. They remembered how they used to find Women's Voices in the past when it consisted of .doc files, starting from the era of desktop computers, starting in high school, reading every day, saving the articles. Some people said that our magazine was their best friend. Some people said that our magazine was alive. Some people said that the death of Feminist Voices felt like the death of a famous singer. Of course, I didn’t make that comparison myself. It was only until after Feminist Voices was gone that I realized that the purpose of creating feminist knowledge was to share and disseminate that knowledge. When one part of our life dies, we take what we are left with and work hard to move on. This is the responsibility of social activists. I will always mourn Feminist Voices. It is such a beautiful name. It carries the enthusiasm, persistence, faith, and love of so many people, and I am proud and sad for it. I will also guard the intellectual riches created by Feminist Voices and strive to ensure that its history is not forgotten. But I can't end on that note. The government may have blocked Feminist Voices, but they cannot block the feminist movement. About a month after the closing of Feminist Voices, the Chinese #MeToo movement set off a new wave of conversation and activism. By August 2018, an unprecedented, shocking tide of feminist activity had taken off —even if participants were not foregrounding the term "women’s rights." (In fact, some people online used emoji to avoid censorship. Instead of posting “#MeToo,” they used the emoji for a bowl of rice (mi) and a rabbit (tu), which together sound the same as “me too.”)
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In 2012, I thought that we were starting a campaign. In 2015, I feared that the campaign was about to fail—I was wrong. In 2018, I finally realized that our campaign was just beginning. The movement is vast and networked: it has no central leadership. But this does not mean that it doesn’t need competent communicators, organizers, and trainers. As I write this article, we have begun to pursue the next stage of our campaign.
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There’s currently a bewildering array of mental health apps to download, with names such as Calm, Happify, and MindShift. Some of these programs are simply collections of user-directed exercises (“write down how you felt today”). Some seek to connect clients with human therapists over video or text chat. Others contain characters like Ellie. But most of them claim to implement elements of cognitive behavioral therapy, or CBT, a kind of therapy that emerged in the 1960s to become one of today’s most heavily used and studied methods of treating depression, anxiety, and PTSD. For many years, CBT has been considered the most effective treatment for such disorders. One recent meta-analysis of mental health app trials found that over 80 percent of them used CBT in one form or another.
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CBT is a heavily “manualized” form of therapy, meaning that it expects providers to stick closely to highly specific, almost scripted interactions with clients. Providers teach clients to recognize negative thoughts and “dispute” them in order to reduce their effect and replace them with positive ones. If the recurrent thought “I’m destined for failure” runs through a patient’s head, for example, CBT prompts them to systematically dispute the thought with alternative interpretations: Is this, perhaps, an exaggeration? Is there really any evidence that this is true? It’s akin to running an algorithm on a thought until its power diminishes and eventually disappears. As a result, a common complaint from those on the receiving end of CBT is that it is cold, mechanical, and lacks empathy. Writing for Vice about the game I eventually made about a virtual CBT therapist, the critic Rob Zacny said he recognized in it “that sense of emotional whiplash that comes from finally starting to confide something serious and scary, only to be met with weirdly programmatic responses from well-meaning counselors.” But the mechanistic quality of CBT also makes it especially well-suited to be supplemented by, or turned entirely into, a computer program. If your human therapist already acts and sounds like an automaton, what would be lost by replacing him with one? But virtual characters can only be effective if on some level we believe or buy into them. In a 2016 article for the Guardian about new questions surrounding CBT’s efficacy, the journalist Oliver Burkeman spoke with a woman who had experienced postnatal depression. “I don’t think anything has ever made me feel as lonely and isolated as having a computer program ask me how I felt on a scale of one to five, and—after I’d clicked the sad emoticon on the screen—telling me it was ‘sorry to hear that’ in a prerecorded voice,” she told him. What she realized she needed, Burkeman writes, was “real connection: that fundamental if hard-to-express sense of being held in the mind of another person, even if only for a short period each week.”
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I get why people find it easier to let machines guess what they want. They are good at guessing! But I think that tendency make people a little less critical about their choices. So I still prefer the more complicated interfaces that take a while to learn, but that let you really think about what parameters you’re interested in and why.
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We’ve talked about using technology to find polyamorous partners—but what about scheduling? I know that one of the challenges of maintaining polyamory relationships is scheduling time with each partner. Has technology helped automate any of that labor? Are there specific tools you use? Carrie: Yes, Google Calendar. Any shareable calendar app with instant updating would do the same work, but this is the one I use. Other technologies, like messenger apps, don’t so much automate the labor of scheduling as make it possible.
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For example, the porn industry has a tremendous amount of capital—and it uses that capital to influence search. One big porn company that owns thousands of websites will have those sites link to each other extensively, in order to bolster those sites’ rankings and dominate a number of keywords. They can also buy interesting combinations of keywords that will guarantee that they will control the representation of women in search, especially women and girls of color. This is why for many years you could do a search in a commercial search engine like Google on the words “black girls,” “asian girls,” and “latina girls,” without even adding the words “sex” or “porn,” and get back pornography. Those identities have been synonymous with porn.
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That’s what happens when we take human decision-making out of knowledge management. We don’t have cataloguers on the web the way we do in a library. Instead, we have people designing algorithms that exert tremendous power over our society but who, quite frankly, have very little understanding of our society.
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The warehouse workers I encountered play games, against themselves or their coworkers. They cheat to artificially boost their productivity numbers. They pass these tricks around in coded language. They use their scanners to find erroneously underpriced items and buy them in bulk. (Some steal outright.) They play (usually harmless) pranks on overbearing managers. And almost all of them skirt safety rules to move faster. Naturally, my sources were hesitant to disclose the specifics of some tactics, particularly those that openly defy Amazon’s rules or the law. In some cases, even when I learned them, I’ve left the details deliberately vague. By far the most common form of resistance among Amazon employees, however, is to quit. A warehouse organizer in Illinois told me that employees who have been around for at least six months are considered “old guard.” For most workers, an Amazon warehouse job is exhausting, deadening, and unsafe. “They work themselves to the bone and wind up washing out,” said Charlie, a fulfillment center worker in Northampton County, Pennsylvania. For those who stay, the draw is Amazon’s generous compensation and benefits package, relative to other low-wage workplaces. “The running joke is that the only benefit to working at Amazon is the benefits,” Charlie told me. For full-time employees, Amazon offers health insurance plans and a 401(k); in October 2018, CEO Jeff Bezos established a $15 minimum wage across its US warehouses. Even before then, Amazon tended to pay better than other employers in the logistics industry.
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Amazon builds fulfillment centers in hollowed-out industrial areas and the exurban fringes of increasingly unlivable cities. Warehouse jobs are often the best (or only) game in town. Even so, workers say, it’s not always worth the trouble. According to Sam Nelson, an organizer with Jobs with Justice, a national coalition of unions and community groups, a frequent refrain among workers at Amazon’s warehouse in Shakopee, Minnesota is, “This is the best job I ever had, and I’m going to quit in two months.” In this context, daily acts of resistance serve as body and sanity-saving strategies. Small workarounds — tricks, games, minor sabotage — extend the time one can bear the relentless and deadening grind. A strategy that saves one’s calves an extra trip across the warehouse could make the difference between quitting this week and holding out for another paycheck. These strategies are not only valuable, then, for privately registering one’s discontent but also for survival. At their limit, they may even be genuinely oppositional: functioning as forms of industrial sabotage or fostering solidarity among the workers. In Weapons of the Weak, Scott writes that everyday resistance enables those he observed to “nibble away” at onerous or unfair policies without risking “more quixotic action.” Even when the oppressed decline mass action, Scott argued, petty insubordinate gestures “create a political and economic barrier reef” on which the ship of state (or capital) might eventually run aground. But there is a danger in over-valorizing the weapons of the weak. You risk conceding that the weak will always remain so — giving up the possibility of collective upheaval. Some acts of resistance actively inhibit mass action. Skirting safety rules and deploying labor-saving tricks may provide the feeling of having pulled one over on the boss, but they often benefit the company. Games displace competition horizontally, among coworkers, and insulate management from labor’s ire. And even those techniques which slow productivity may merely function as safety valves, preventing the buildup of more acute and collective rage.
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As such, Occupy acted as a bridge between two movement media paradigms, combining aspects of the Indymedia model with the tactical use of social media. But in subsequent years, as Occupy receded, the last vestiges of the Indymedia legacy would disappear. As social media’s dominance grew, it began to take on an ever greater role in the media strategy of social movements, from Black Lives Matter in 2013 to Standing Rock in 2016, all the way through the 2020 uprising against the murder of George Floyd, Breonna Taylor, and Ahmaud Arbery. The dependence on social media came to be total.
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Superficial Solidarities Social media has obvious advantages for organizers. It can be used to galvanize attention around an issue and scale up a protest quickly. New participants can be inspired to take action as activist-commentators at unprecedented speed and scale, and in the process, generate extensive global solidarity. As Sarah J. Jackson, Moya Bailey, and Brooke Foucault Welles argue in #HashtagActivism, social media also gives movements the chance to make important narrative interventions. In some cases, movement use of social media can change the very terms used to frame issues, as with the George Floyd rebellion, which ushered in the wider acceptance of “white supremacy” as part of the conversation surrounding police violence. But social media also exposes movements to many vulnerabilities. The solidiarities it generates are often superficial: movement use of social media can easily devolve into repetitive messaging in echo chambers without collective gains in narrative power—a change in the stories and values that hold sway in society—or a translation to real-world militancy. In all cases, the logic is determined not by a radical politics of participation and organization, as in the Indymedia model, but by the individual user’s decision to follow, like, comment—in short, pick and choose (within the ruse of algorithmic “choice”) which leaders to listen to and which profiles to amplify. This short-circuits the important but slower work of belonging to—and being responsible to—a movement culture. The small doses of interpersonal connection that social media platforms are built to deliver stand in for collective gains of social power. Corporate social media platforms are governed by a capitalist logic that exists to make profit, while rewiring our emotional and cognitive dependencies toward that end. Social movements may recruit people through social media, but without real relationship-building, without a sense of shared responsibility, the commitments it mobilizes are weak. Moreover, the information that activists share on social media flows within a broader stream of information about anything and everything, which risks distraction, trivialization, and co-optation. On social media, movements must deal with the constant threat of having their agenda and messaging hijacked by corporate interests, politicians, funding foundations, and mainstream media—all of which was on full display during the George Floyd rebellion.
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