Prompt: The likelihood of very little interference is why Green Bank was selected in 1954 as the location for a new radio astronomy observatory facility. It’s close to Washington DC, yet sparsely populated and conveniently protected on all sides by the Allegheny Mountains. But just in case, lawmakers drew a square on a map of the West Virginia-Virginia border, and the National Radio Quiet Zone was born: 13,000 square miles within which all devices that emit microwaves and radio waves on a large scale are severely regulated. Cell towers and radio stations do exist throughout the Quiet Zone today, but they must be directionally pointed away from the telescope and every request for a new one is subject to observatory approval. Within a ten-mile radius of Green Bank, however, cell towers and Wi-Fi networks are banned.

Response: We are told that when we board the school bus that will take us to the telescope, our cell phones must be switched off. Even though there is no cell reception here, our phones are tiny transmitters that will keep searching for a signal, emitting radio waves all the while. Dave steps up onto a thin strip of stage and wheels over a cage connected to a small screen. The cage is a Faraday cage, which blocks all electromagnetic radiation, and the screen is a spectrum analyzer which shows how much radiation a given object is spitting.


Prompt: He puts a digital camera circa 2000 into the cage and a spike like an EKG appears on the analyzer’s previously flat line. “Whoa,” says Dave, “don’t take that near the telescope.” Then he takes a small handheld fan and repeats the experiment. The analyzer goes bonkers. “We used to sell them here in the gift shop,” Dave says, “until we realized.” We are loaded onto a shoddy school bus, shoddy on purpose—only diesel vehicles are allowed past the visitor’s center, since modern cars have computers, spark plugs, and more. At 485 feet and a parabolic surface area of 2.3 acres, the Green Bank Telescope is visible from miles away—a giant white satellite dish atop a giant white crane. Up close, the rain rolls off its listening panels and it seems too hard and inanimate to be that sensitive—the most electrosensitive body in the world.

Response: Taking Precautions When Carmen Scherrer was forced to flee her home in Virginia Beach, she went to the woods. At first she just walked around, trying not to die. Then she started noticing the birds. Watching them preen and pop made her feel a little better, if only by distraction. She bought a Sibley Guide to Birds so she could identify them based on the shapes of their heads. A red-winged blackbird, a yellow finch.


Prompt: Fifty cents, one fumbling call on the observatory’s payphone, and twenty minutes spent swinging my legs on the sunshiney bench outside the gift shop and a black SUV is rolling up containing Carmen and Diane. I have kept my phone totally off and been sure to wear clothes that have not been laundered with scented laundry detergent, per Diane’s instructions.

Response: There are two kinds of electrosensitive people, I decide—the ones who seem normal, but for God’s grace go I, and the ones who don’t. Carmen is the former, Diane is the latter. Part of the electrosensitive PR problem is that the strange ones get so much of the air time. “Well hello there,” Diane says from the passenger seat, “I thought you would have called sooner, I’ve been waiting the whole morning.” Long grey hair in pigtails, Tevas worn with socks, loose slacks.


Prompt: We’re on our way to look at a rustic cabin in a nearby state park to see if it might be a suitable place for Carmen to live temporarily and host her son and parents. She has been coming to Green Bank (even the town’s name sounds better with her rolled R) for two years, just on weekends or a day or two when she is feeling extra sick. But no one in her family has ever come with her.

Response: Diane directs Carmen to drive west on the two-lane road that runs through Green Bank, and she obeys, driving slowly. Carmen emigrated from Spain to marry her husband, who she’d met as a high school exchange student. She is slim and tan with long dark hair and a soft-looking denim shirt. Diane is used to tour-guiding and does it well, pointing out Green Bank Middle School, which abuts the observatory property. Unlike most schools these days, this one doesn’t have any Wi-Fi, only hardwired computers.


Prompt: “Thank God,” says Carmen. The question of Wi-Fi in schools has received some mainstream attention. A 2014 article publishing the results of a study conducted by researchers at the University of California San Diego made the case for limiting children’s exposure to EMR when possible, including removing Wi-Fi from American public schools. In two Canadian cities, parents petitioned unsuccessfully to have the Wi-Fi removed from their children’s schools.

Response: Over and over, many scientists and laypeople have pointed out that there is no hard evidence that links EMR exposure to the kinds of health effects that electrosensitives describe. But after a documentary about the health effects of Wi-Fi on children was shown widely in Israel, the mayor of Haifa announced that it was immediately removing Wi-Fi from its public schools, saying, “When there is a doubt, when it comes to our children, there is no doubt. We must take excessive precaution.” It’s this kind of precaution that Martin Blank, a retired professor in the Department of Physiology and Cellular Biophysics at Columbia University, argues for in his book on the subject, Overpowered. “Just as the United States became the first nation in the world to regulate the production of chlorofluorocarbons (CFCs) when science indicated the threat to earth’s ozone layer—long before there was definitive proof of such a link—our governments should respond to the significant public health threat of EMF exposure,” he writes.


Prompt: Diane also points out the Green Bank Library, where most of the electrosensitives in town come to check their email. Like the middle school, the library too has only wired computers. Diane also has a wired computer at home, which she uses to answer emails from electrosensitives the world over, stay up on the newest scientific studies about the health effects of EMR on humans—which she prints and organizes in file folders on her shelves according to their year and location—and look at baby pictures of her grandson. Most electrosensitives are not ideologically anti-technology. They just want a different relationship with it: to be able to opt out when necessary.

Response: “I can’t go into that Dollar General,” Diane says as we pass it, “because of the fluorescent lights. I asked them to turn them off, but they won’t.” “If I have diabetes, if I have cancer, I don’t have to tell everyone about it. I don’t want to bother anybody, I just want to live my life,” Carmen says. “But this illness is not like that. Other people’s habits affect us, they don’t stop at the border line. It blows toward us.” House Hunting Carmen stops the spotless Honda CRV she is driving on the crest of a one-lane road and points across a technicolor green field at something that neither I nor Diane can see.


Prompt: “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.

Response: “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.


Prompt: Then the attacks started happening at her kids’ school, in the supermarket, even in her own home. She now attributes the attacks to the fluorescent lights that are ubiquitous in public schools, as well as to the critical mass of cell phones, Wi-Fi, and the effects of a nearby cell tower. She disabled her Wi-Fi network, removed the electricity in whole swaths of her home, replaced all compact fluorescent bulbs with regular bulbs, got rid of her cell phone, and asked her children and husband to keep their phones in their cars and charge them at neighbors’ homes.

Response: “Usually when a person has a sickness they get support, that’s part of what helps them get better,” she tells me, tearing up as she drives on a cool, laurel-covered road through the state park. “Not with this. People not understanding. You have to quit working, you have to quit socializing, you have to quit your previous life. Nobody believes. You feel this loneliness. When I am calling Diane, I am just crying, saying, ‘Believe me!’” We arrive at the cabin that Carmen wants to check out.


Prompt: “Oh! This is niiiiice,” Carmen says, nosing around the place. It has an iron wood-fire cook stove, and gas refrigerator. All of its lights are kerosene and must be lit by a match. But Carmen stiffens. “I think I am picking something up a little bit here,” she says. “Yes, something is bothering me.” The state park, which Diane has directed many of her electrosensitive guests to, also has tent camping sites. Diane tells Carmen to keep driving to the most secluded camp site, which sits near a river.

Response: “Aha,” says Carmen, “so even if another family is having a cell phone maybe it will not bother me too much because we are so far over here.” She paces around, taking pictures. “Yes I feel much better over here. I am not feeling so good at the cabin but I feel good here.” Diane instructs Carmen to turn left off the two-lane state road and heads up a steep dirt driveway that’s been cut—recently, it seems—into the side of a mountain. The road dead-ends in a clearing where a cinderblock foundation and some framed walls are open to the sky. A woman and a man stand in the foreground, both thin and tan, bent over the open rump of a car rummaging around inside it.


Prompt: Sue’s long brown hair is up in a girlish ponytail. Her breathable shorts and hook-and-eye boots make her look more like a thru-hiker than what she is: an electrosensitive woman fleeing for her life. John—-denim work pants, a flat pencil behind his ear, drinking water from a jar—looks like he could be a local, only the New York plates on their car give him away.

Response: Sue is not only electrosensitive, but chemically sensitive, a combination that is common, she tells me, as we settle into folding chairs in a circle of shade. She is affected by all kinds of artificial smells and substances, from treated wood to DEET to perfume, hence the instruction about my clothes. John is not electrosensitive himself, but has been supportive of Sue, a rarity I learn—electrosensitivity seems to kill most of the marriages it touches.


Prompt: Sue and John are from Mamaroneck, a prosperous satellite suburb of New York City. Sue thinks it started when the utility company installed a smart meter— equipped with wireless communication so it can check rates remotely—on the back of their house. Either that or it was their new neighbor who had every gadget and every light on a dimmer switch (dimmers generate much more EMR than traditional light switches).

Response: “When he was home, I couldn’t be in my house,” Sue says. Her skin would burn, and she got immobilizing headaches. When she used her phone, her ear would get hot. Her fingers tingled when she tried to use a computer. A new cell phone tower went up right over their kids’ school. Sue’s toes would bloat and burn, which she illustrates with a photo on her digital camera. They do indeed look puffy and red, as if from a very bad sunburn.


Prompt: They hired a professional to shield their bedroom, to make it as much like a Faraday cage as possible—three layers of aluminum mesh. Often she went whole days without leaving her cage room. “My life became very small,” Sue says. She couldn’t socialize with friends or attend events at her children’s school. She missed her son’s high school graduation. “Too many cell phones!” she cries. “Out of a class of 500, our son is the only one who didn’t have a cell phone.” But eventually even the cage didn’t help her. She couldn’t sleep. “I would bring her out to the car and she would try and sleep in the car,” John says.

Response: Soon she took to driving her car to the farthest reaches of town and parking it under an I-95 underpass to get relief. That’s when their daughter called. She had read an article about Diane and Green Bank. The couple came down, then bought land here shortly thereafter. The plan is to build themselves a new house, with green chemical-free materials custom-tailored to Sue’s sensitivities, and a separate shed for the appliances.


Prompt: While their house is being built, Sue and John are camping on a piece of land owned by WAVR, a nonprofit that Diane and others started to provide free lodging to electrosensitive people and their family and friends. Right now it’s a grassy field, two sleeping cabins, and a hunter’s lodge with a communal kitchen.

Response: Sue would like to see a swath of land in every country put aside as EMR-free zones for electrosensitives to go. As it is now, she says, “there’s just no way to opt out.” An Experiment Until as recently as the 1980s, coal miners brought caged canaries down into the seam tunnels with them. As long as the canaries were singing, things were copacetic. But if the bird passed out or died, that signaled unsafe levels of poisonous gas in the air—time to run. Because canaries need double the oxygen of most birds—they take it in upon exhale as well as inhale—they made ideal air testers.


Prompt: The phrase “canary in the coal mine” has become so overused as to be effectively meaningless. But when talking about electrosensitives, the comparison is appropriate. The working theory, according to the electrosensitive community and the scientists who support them, is that those with EHS are the same yet different from all of us. Something about the way they are built makes them especially porous, especially sensitive—so whatever is slowly killing the rest of us, the theory goes, is killing them much faster.

Response: Just before the bubonic plague started killing millions in medieval Europe, people reported seeing rats keel over in the street with unexplainable frequency. We study honey bee death as an important sign of air pollution, and bivalve molluscs for water pollution. Implicit in these strategies is an ethical position: it is acceptable for some life to die so that humans can live or better understand the threats stewing in their environment. But what if the harbingers are people? Electrosensitives say they’re not fundamentally different from people who don’t experience health effects from EMR—they are simply more sensitive.


Prompt: “We’re gifted,” Diane says. “If this stuff is bad for Sue, it’s probably bad for all of us,” John says. “You may not realize it, but you are participating in an unauthorized experiment,” Martin Blank writes in Overpowered. “For the first time, many of us are holding high-powered microwave transmitters—in the form of cell phones—directly against our heads on a daily basis…What health effects do these exposures have? Therein lies the experiment.” Blank writes about how EMR, even at very low levels, interrupts bird and bee navigation, and can cause bee colonies to collapse. In one study Blank cites, researchers put an active cell phone in front of a bee hive, and in short order, the hive crumbled. A 2010 Dutch study tied a mystery illness decimating European trees to Wi-Fi radiation.

Response: While Blank has some allies, his opinions are not popular in the scientific community because they go against a foundational idea upon which much of modern technology is based: sure, X-rays and ultraviolet radiation are carcinogens, known to harm human cells (which is why we wear protective gear when getting our cavities photographed), but that’s because they are ionizing radiation (short-wavelength, high-frequency).


Prompt: As long as the EMR created by a given technology is non-ionizing—long-wavelength and low-frequency, such as microwave and radio—it’s not harmful to humans. But Blank’s research was showing that even non-ionizing radiation, which has much less energy than X-rays, was affecting a very basic property of cells—the ability to stimulate protein synthesis.

Response: To accept this conclusion, many scientists would have to throw out years of research. Not to mention, Blank archly implies, sever their funding ties to the tech and telecommunications industries. Blank tells an anecdote of a scientist who challenged his research at a conference—the scientist, he later found out, was employed by a major cell phone provider.


Prompt: Conspiracy theories aside, Blank is sympathetic. “People simply cannot bear the thought of restricting their time with—much less giving up—these beloved gadgets,” he writes. “This gives industry a huge advantage because there is a large segment of the public that would simply rather not know.” Hardware vs Software Another theory of EHS is that the sufferers’ symptoms are real, but the cause is people’s anxiety and stress in response to the idea that wireless technology is harming them, rather than any “actual” harm.

Response: For a disease to be real, we are told, proof of physical malfunction is required. If none can be found, the suffering is deemed “psychosomatic,” and left at that. But within that word is the word “somatic”—-of the body. Suzanne O’Sullivan, a British neurologist and the author of It’s All in Your Head, regularly diagnoses patients as suffering from psychosomatic illness, but that doesn’t lessen the seriousness of their symptoms or their treatment. Psychosomatic disorders, she says, are physical symptoms, truly experienced, that mask emotional distress.


Prompt: “It feels real because it is real,” O’Sullivan tells a man who used the internet to diagnose himself with multiple sclerosis. “Your paralysis is not imagined but that does not necessarily mean that it is a primarily physical disorder.” Ultimately, the man accepts this and applies his own analogy: “My hardware is intact and the wires are all in the right place, but I have a software problem that stops my legs receiving the instruction to move.” As a culture, we are troubled only by hardware problems, and inclined to dismiss those that occur in our software, despite the fact that both can and do cause glitches and crashes. We also tend to use these words—hardware, software, body, mind—as if the boundaries between them are securely distinct. It feels necessary to distinguish between different kinds of distress.

Response: But one thing is clear: new technologies are causing some humans to experience distress, in one form or another. It just so happens that at the time of my trip to visit Green Bank, I had recently endured a disappointment in love. Let’s call my ex-beloved F. F and I had built a life, and then just like that it was gone, as is prone to happen. I had been hitting the tarot and journaling hard. The hours between three and six in the morning had come back to me and it was difficult to sit still by myself without water squeezing out. “I feel like poison is coursing through my veins,” I wrote.


Prompt: The metaphors we use for anxiety and grief are often electrical. “My entire body buzzed as if I’d been plugged into an electrical socket,” writes psychotherapist Leslie Carr in “What Your Anxiety Is Really Trying to Tell You.” “Anxiety is your body’s way of trying to get your attention,” Carr goes on. “It’s an invitation from your unconscious, alerting you to something in your emotional life that needs to be heard.” After I’ve dropped off Diane and Carmen at Diane’s house, I drive away through fields buzzing with katydids. I turn up the radio and hear that President Trump has officially pulled America out of the Paris climate agreement.

Response: Google “ecoanxiety” and the results are profound. Not only does it refer to the fact that scientists are now tying climate change to increased rates of stress, anxiety, depression, and suicide due to storms and slides and tsunamis, but it also hints at something deeper and more amorphous—you might even say somatic.


Prompt: At the core, we are animals destroying the resources we need to survive. How much to compromise so that the needs of all are met is at the heart of this whole situation. “Accommodations” is a word often in Diane’s mouth. How would one build a community that preserves modern technology while accommodating electrosensitives? Ideally it would be a both/and—a choice we can all make for ourselves. Those of us who can’t tolerate or don’t want connected technology to subsume our experience should be able to opt out. Those who need or want it should be able to opt in. But currently it seems that no one gets to choose—that, for a multitude of reasons, connected technology is a layer of living that eludes personal choice.

Response: I do not know what exactly the sickness of EHS sufferers can tell the rest of us about how we live. But I do believe that it is meant to tell us something. In 2010, Adam Abdullah walked free from prison after thirty-three years inside. He can still recall the sensation of rejoining a society that in his absence had put a computerized telephone the size of a candy bar in the hands of a woman across the aisle from him on the bus home: She kept moving her fingers and I thought, “What the hell is she doing?” Then it dawned on me: That’s texting. For hours, just tapping that little phone.


Prompt: Technology in its present-day networked and connected form poses problems in a setting conceived to separate from society those deemed to have abused its freedoms. Apart from intermittent access to an offline PC, Abdullah, convicted of kidnapping while armed, was largely sealed off from the digital technologies that have revolutionized life on the outside. Today, he’s a cofounder of Denver’s Second Chance Center, helping ex-inmates get back on their feet. But the metaphor he employs for his time inside is grim: “We were goods, like a can of spam put up on a shelf.” Seven years on, consumer devices of the kind that awed Abdullah are proliferating in prison. This technology aligns squarely with penal reform goals espoused by a loose-knit neoliberal coalition of fiscal conservatives and moderate Democrats. Forty years after the vertiginous run-up in the prison population began, criticism of mass incarceration is no longer the preserve of radicals.

Response: These groups have found common cause in getting “Smart on Crime” (as opposed to just tough) and bringing ballooning correctional costs to heel, as University of Pennsylvania political scientist Marie Gottschalk has documented. Vendors of tablets position their products as instruments for enacting this agenda. They tout them as a tool to help rehabilitate inmates, ease their re-entry into society, reduce recidivism, and pare cost.


Prompt: It’s hard to quibble with the potential these devices hold to enrich and empower an often-despairing population, as Abdullah attests to. But early evidence suggests that the language of reform offers convenient cover for business practices that prey on prisoners and their families. Moreover, even when deployed with regard to inmates’ rights, technology is no panacea for the ills of a criminal justice system that locks up Americans with the same alacrity that Stalin sent citizens of the Soviet Union to the Gulag. In fact, it’s possible to regard the focus on a technological fix as a distraction from the real steps needed to end mass incarceration—and even as a symptom of the neoliberal preoccupation with budget cuts that helped get us here.

Response: Inside the Hair Shirt The “digital divide” is a phrase less on our lips these days, though pockets of dislocation from the information age stubbornly persist. But if there’s a populace among whom it remains starkly operative, it’s the more than 2.17 million inhabitants of America’s carceral state. According to the 2017 edition of the Columbia [University] Human Rights Law Review-produced Jailhouse Lawyer’s Manual, “Most states ban prisoners from direct, unsupervised access to the Internet.” Fears of bad actors hacking a website, downloading porn, taunting a victim, getting to a witness, arranging a drug drop, or even ordering a hit—pick your scenario—loom large.


Prompt: Reinforcing such concerns, prison leaders are acculturated to view innovation with suspicion, cued to the worst case and inclined in most matters to default to security considerations. “When you work in corrections, you tend to be risk averse,” says Pennsylvania Secretary of Corrections John Wetzel. For good measure, technology has characteristically been deemed a luxury of which prisoners are undeserving—certainly nothing the public would pay for.

Response: Accordingly, there’s a perfunctory hair-shirt aspect to prison technology. It stands in relation to free-world technology as prison chow does to a restaurant meal or prison-issue fatigues do to a tailored suit. Proprietary prison email systems allow inmates to correspond with approved contacts. But in- and outbound messages are typically subject to fees—between twenty-five and sixty cents, according to Alex Friedmann, associate director of the Human Rights Defense Center and managing editor of Prison Legal News. And stingy length limits apply. These range from 1500 to 6000 characters, at the lower end of which Martin Luther King Jr. would have had to piece out his “Letter from a Birmingham Jail” on an installment plan, attorney Stephen Raher notes.


Prompt: Walled Gardens “Corrections-grade” tablets follow stolidly in the tradition of penal products as drabber, dowdier versions of their free-world counterparts. They typically come sheathed in bulbous clear plastic bumpers to offer contraband no quarter and withstand use in a “rugged” environment. And they offer users a locked-down “walled garden” of content such as AOL in its heyday could scarcely have dreamt of. Whereas AOL users quickly learned they had only to unlatch an HTML gate to gambol free on the open internet, prison tablet content resides on on-site servers whose robust security, coupled to safeguards on the device itself, are said to render the threat of a break for digital freedom strictly theoretical.

Response: But there’s much that prisoners can do on them. For their intellectual edification and personal and professional growth, they can browse educational modules—GED lessons, vocational training, courses in cognitive behavioral therapy, or help with addiction. Voice or Skype-like video calls can be placed to friends and family, or inmates may send them email. And they can listen to music, read ebooks, watch movies, or play games. To manage everyday life, they can make requests of and lodge grievances with correctional staff, order commissary items, and schedule visits from loved ones.


Prompt: These capabilities deliver on key tenets of neoliberal prison reform. Two out of three ex-prisoners are rearrested within three years of release. Education interrupts the cycle. Inmates who take classes are 43 percent less likely to reoffend. Tablets can extend educational offerings to prisoners at “zero marginal cost,” notes David Fathi, director of the ACLU’s National Prison Project.

Response: They can also serve as digital umbilici, nourishing “social capital” shown to fortify inmates during imprisonment. Studies in Florida and Minnesota linked staying connected to loved ones to reduced recidivism. The findings relate to in-person visits, but prisons are frequently remote from inmates’ homes—hard for friends and family, many of whom are working poor, to reach. Videoconferencing—“video visitation” in correctional parlance—is a boon, allowing prisoners to see loved ones as they talk.


Prompt: Moreover, in an environment that otherwise affords scant scope for control over one’s affairs, it has to feel empowering to exercise at least a modicum of autonomy by using tablets’ administrative capabilities. Finally, it’s only common sense to familiarize prisoners with technology they’ll need to be fluent in to find work, secure services, and generally navigate life post-release.

Response: Propelled by such use cases, eleven states (and multiple local jails) now offer tablets, with up to three more expected to greenlight them or go live by the end of the year. And, though its current status is unclear—the initiative appears on a Department of Justice web page labeled “archived content” since Trump entered the White House—last year, the Federal Bureau of Prisons announced a “pilot program [to] be rolled out at two prisons in early 2017 and… expanded to additional sites in future years.” Carceral Capital But, for all the potential tablets hold, there are reasons to be skeptical of the vendors selling them.


Prompt: The core business of GTL and Securus Technologies, the companies that dominate prison information and communications technology, and substantially control its nascent tablet market, is prison phone systems. With Telmate (since acquired by GTL), they presided over prison payphone rates of up to $17 for fifteen minutes. The FCC capped interstate rates at 25 cents a minute in 2013, but the companies successfully appealed price controls on in-state calls, which make up most of their business. As of late last year, these ran as high as more than $1.50 per minute, according to advocacy group the Prison Policy Initiative (PPI).

Response: Prison tablet deployments are often “bundled” with phone service contracts. As such, they’re subject to the same industry practice under which prisons collect “commissions”—typically a cut of contractual revenue—from vendors. These commissions are presented as a way to subsidize inmate programing, but they’ve actually hurt inmates by contributing to inflated payphone rates. They also offer a lever that companies use to land exclusive access to prisoners. A 2015 tender obtained by The New York Times revealed how Arizona’s Department of Corrections scored bids to run its phone system: 1250 points to the contractor dangling the largest commission versus an aggregate of 300 points for all other criteria.


Prompt: Underwriting this arrangement are inmates and their families. GTL bears the cost of installing the infrastructure for tablet deployment and then distributes its devices to inmates free (with exceptions: in Pennsylvania inmates pay $147 for theirs), charging them for access to content. Subscriptions typically start at 99 cents per day and go up to $25 for a month, says executive director of inmate applications and hardware Brian Peters. Other fees are transactional: for example, 25 cents for text-only email, 50 cents to send messages with an attachment, and $1 for one with embedded video. Securus didn’t reply to an interview request, but access to one of its tablets commands a monthly fee of $15 to $45, while another costs an undisclosed sum plus charges for downloadable content.

Response: This model has the signal merit of getting taxpayers off the hook. The political untenability of asking us to pay for anything that appears to cosset inmates is considered axiomatic. “The public doesn’t want to pay,” says Martin Horn, who led the Pennsylvania and New York City Departments of Correction in the late 1990s and mid-2000s, respectively. For a glimpse of the injunctive hold of this supposed truism, look no further than the disclaimer the Federal Bureau of Prisons felt obliged to issue on the first web page that comes up when you Google “TRULINCS,” acronym for its Trust Fund Limited Inmate Computer System, an email service: No taxpayer dollars are used for this service. Funding is provided entirely by the Inmate Trust Fund, which is maintained by profits from inmate purchases of commissary products, telephone services, and the fees inmates pay for using TRULINCS.


Prompt: But it’s worth considering who benefits from this posture, enabling as it does a lucrative business and fulsome commissions for prisons. GTL and Securus are owned by private equity outfits, and Securus’ current owner Abry Partners stands to make a handsome premium on the $640 million it paid for the company in 2013 if the FCC approves its $1.6 billion sale to Beverly Hills-based Platinum Equity. Meanwhile, commission receipts paid to prisons added up to more than $460 million in 2013—a windfall put toward “roads…staff salaries [and] state or county’s general budget,” among other items, in addition to “inmate welfare,” according to the FCC.

Response: Peters points to the “significant capital burden” of outfitting “an entire building with a wireless network…and handing every prisoner a tablet.” But a copy of GTL’s 2015 contract with Colorado state prisons obtained by PPI is a feat of legal and financial engineering. For $800,000 a year, GTL has “the exclusive right to collect and retain all revenue generated from the services supplied through this Agreement.” Beyond contractually fixed phone charges, the company “may in its discretion change any pricing,” the document adds. This includes “up to $19.99 per one month subscription” for a music streaming service—twice the price of Spotify for a fraction of the content if the catalog available to Pennsylvania inmates is any guide, PPI notes. The contract also permits GTL to levy fees on electronic transfers of money deposited in inmates’ accounts to pay for services—a widespread industry practice.


Prompt: Securus, for its part, has shown little compunction about trampling inmates’ interests when this conduces to maximal profit. According to PPI, the company inserted clauses in contracts requiring some prisons deploying video visitation to cease in-person visits—forcing inmates and their loved ones to pay for its service as their only means of seeing one another—before backing off amid outcry.

Response: The Disruptors Are Coming The prison technology market certainly seems ripe for disruption. This is what American Prison Data Systems (APDS) and Edovo (a contraction of the suitably aspirational “education over obstacles”) are banking on. Both push tablets loaded with gamified education platforms that prisons, not inmates, pay for. Instead, inmates earn credit for completing educational modules redeemable for entertainment options.


Prompt: Both also flaunt their social conscience. APDS advertises its certification as a public benefit corporation committed to “social impact” besides the bottom line. Edovo bills itself as a “criminal justice reform and prison tech company.” APDS’s backers include ex-Citigroup CEO Vikram Pandit and former Thomson Reuters CEO Tom Glocer. Edovo recently snagged the Global EdTech Startup Award.

Response: These start-ups will surely find it challenging competing against rivals bestowing commissions. But research from England—a reasonable, if imperfect, analog to America—suggests wriggle room beyond taxpayers’ reflexive opposition to prison technology that might embolden officials to put public money on the line. A poll of English attitudes to “prisoners’ access to digital technology” by De Montfort University researchers encountered familiar squeamishness. But 57 percent said access could be “earned,” half viewed technology as rehabilitative, and 44 percent anticipated “efficiency savings” that would “save them money.” Another 25 percent were “undecided.” “Most of the public is ambivalent,” says co-author Victoria Knight. “They have a reaction, ‘prison is for punishment,’ but when you dig deeper they can be swayed. There’s room to inform.” A little over three years after their launch, APDS and Edovo are in forty-plus institutions each. There’s much to like about their approach. They embrace transparent costs instead of palming them off on the poorest and least powerful among us. They compete on technology rather than how much they’re willing to pay for access to a captive market.


Prompt: And by placing education at the fore, they depart from a model in which tablets emulate the soporific function of TV in prison—a digital, rather than electronic, “babysitter”—as the private equity emphasis on monetizable content tends toward. They even gesture toward a more expansive sense of social mission on the part of prisons—one that might incline them to straighten up from their defensive crouch on technology.

Response: Beyond Technocracy Ultimately, however, we shouldn’t conflate prison tablets with genuine penal reform, even if the companies behind them have relatively enlightened business models. In fact, with its narrow-gauge neoliberal focus on trimming costs, the enterprise might even be considered inimical to reform.


Prompt: Tablets are frequently positioned as a solution to runaway spending. This perpetuates a false narrative about the fiscal burden of incarceration. While prisons represent the second-fastest growing line item in state budgets, they account for a fraction of overall expenditure—half of what states devote to highways, writes University of Pennsylvania’s Gottschalk. The same cost-saving mania arguably contributed to the dynamic that landed many inmates in prison to begin with by gutting social welfare programs, she adds.

Response: And the “fixation” on reducing recidivism props up spurious notions about the deterrent effect of incarceration on crime—the same notions that fueled the policies behind mass imprisonment, Gottschalk notes. The National Research Council has concluded, she observes, that the “increase in incarceration may have caused a decrease in crime, but the magnitude is highly uncertain and the results of most studies suggest it was unlikely to have been large.” More fundamentally, Gottschalk excoriates neoliberal reformers for failing to make the case for shrinking the prison population as a humanitarian imperative unrelated to economic concerns. This would be a starting point for the practical steps entailed in actual reform: rescinding draconian laws that put so many people behind bars in the first place, restoring voting rights and benefits entitlements for ex-prisoners currently deprived of them, and spending money, without the prospect of immediate returns, on mental health, drug treatment, and other services to help them pick up their lives.


Prompt: Absent such action, the carceral state—infinitely resourceful and protected by entrenched interests—seems here to stay. Initiatives to spare minor drug offenders prison time have garnered much attention, but other crimes pick up the slack, perpetuating mass imprisonment. In 2014, the Pew Charitable Trusts forecast a 3 percent uptick in the state prison population by 2018.

Response: We shouldn’t be lulled by bromides about technology into normalizing the ugly exceptionalism of our criminal justice system. We’re home to 5 percent of the world’s population yet 25 percent of all prisoners. The way to change this brute fact is quite unambiguous. It requires political leadership, rather than prostration before the most reactionary elements of public opinion, and it cannot be outsourced to private vendors. Deployed with an emphasis on education and not outsize profit, prison tablets are eminently worthwhile. But as a substitute for real penal reform, they’re so much technocratic solutionism.


Prompt: Machine learning is the practice of training algorithms to classify and predict in order to support decision-making. In recent years, it has skyrocketed in popularity and ubiquity. It’s no stretch to say that most services we use now incorporate machine learning in one way or another. In its pervasiveness, machine learning is becoming infrastructural. And, like all infrastructure, once it matures it will become invisible.

Response: Before that happens, we should develop a way to disrupt it. A relatively nascent field called “adversarial machine learning” provides a starting point. Described as the intersection of cybersecurity and machine learning, this field studies how these algorithms can be systematically fooled, with or without knowledge of the algorithm itself—an ideal approach, since the specifics of many algorithms are trade secrets. And given the fact that the machine learning regime effectively makes all of us its workers—most of our online activity is in fact labor towards the improvement of these systems—we as individual users have an opportunity to inflict major sabotage.


Prompt: Consider an early and now-ubiquitous application of machine learning: the everyday spam filter. The job of the spam filter is to categorize an email as either “spam”—junk—or “ham”—non-spam. The simplest case of adversarial machine learning in this context is constructing an email that is spam—a pitch for a pharmaceutical product, for example—but in such a way that the spam filter misclassifies it as ham, thus letting it through to the recipient.

Response: There are a variety of strategies you might employ to accomplish this. A relatively simple one is swapping out the name of “Viagra” for something more obscure to a machine but equally readable to a human: “Vi@gr@”, for example. Today, most spam filters are resistant to this basic obfuscation attack. But we could consider more sophisticated approaches, such as writing a longer, professional-looking email that hints at the product without ever explicitly mentioning it. The hint may be strikingly obvious to a human, but incomprehensible to a spam filter.


Prompt: A spam filter is less insidious than many other applications of machine learning, of course. But we can generalize from this example to develop techniques for disrupting other applications more worthy of sabotage. Poisoning the Well Most machine learning models are constructed according to the following general procedure: Collect training data.

Response: Run a machine learning algorithm, such as a neural network, over the training data to learn from it. Integrate the model into your service. Many websites collect training data with embedded code that tracks what you do on the internet. This information is supposed to identify your preferences, habits, and other facets of your online and offline activity. The effectiveness of this data collection relies on the assumption that browsing habits are an honest portrayal of an individual.


Prompt: A simple act of sabotage is to violate this assumption by generating “noise” while browsing. You can do this by opening random links, so that it’s unclear which are the “true” sites you’ve visited—a process automated by Dan Schultz’s Internet Noise project, available at makeinternetnoise.com. Because your data is not only used to make assumptions about you, but about other users with similar browsing patterns, you end up interfering with the algorithm’s conclusions about an entire group of people.

Response: Of course, the effectiveness of this tactic, like all others described here, increases when more people are using it. As the CIA’s Simple Sabotage Field Manual explains, “Acts of simple sabotage, multiplied by thousands of citizens, can be an effective weapon…[wasting] materials, manpower, and time. Occurring on a wide scale, simple sabotage will be a constant and tangible drag on…the enemy.” Attacks of this sort—where we corrupt the training data of these systems—are known as “poisoning” attacks.


Prompt: The Pathological and the Perturbed The other category of adversarial machine learning attacks are known as “evasion.” This strategy targets systems that have already been trained. Rather than trying to corrupt training data, it tries to generate pathological inputs that confuse the model, causing it to generate incorrect results.

Response: The spam filter attack, where you trick an algorithm into seeing spam as ham, is an example of evasion. Another is “Hyperface,” a collaboration between Hyphen Labs and Adam Harvey, a specially designed scarf engineered to fool facial recognition systems by exploiting the heuristics these systems use to identify faces. Similarly, in a recent study, researchers developed a pair of glasses that consistently cause a state-of-the-art facial recognition system to misclassify faces it would otherwise identify with absolute certainty.


Prompt: Above: the input of people wearing the glasses designed to confuse facial-recognition software. Below: the misidentified individuals. Another domain of computer vision is object recognition. If facial recognition is about recognizing faces, object recognition is about identifying objects. For instance, you might ask an object recognition system to look at a picture of a panda and identify the panda.

Response: Recently, computer science researchers developed a “universal perturbation algorithm” capable of systematically fooling many object recognition algorithms by adding relatively small amounts of noise to the image. This noise is almost imperceptible to a human, but causes the object recognition system to misclassify objects. For example, an image of a coffee maker that has been treated with the universal perturbation algorithm will look like a macaw to the object recognition system.


Prompt: Above: the original, correctly classified inputs. Below: the incorrectly classified inputs, post-perturbation. The writer Evan Calder Williams defines sabotage as: the impossibly small difference between exceptional failures and business as usual, connected by the fact that the very same properties and tendencies enable either outcome.

Response: Serendipitously, this is the literal mechanic by which the universal perturbation algorithm works. An object recognition neural network, for instance, takes an image and maps it to a point in some abstract space. Different regions of this space correspond to different labels—so an image of a coffee maker ideally maps to the coffee maker region.


Prompt: But these regions are irregular in form. The categories of macaw and coffee maker may be very similar in certain dimensions. That means it takes only slight nudges—accomplished by introducing the noise into the image—to push a coffee maker image into the macaw region. One advantage of the perturbation algorithm is that its distortions are invisible to the human eye. Sabotage that sticks out as sabotage is not very successful. Well-executed sabotage leaves doubts not only about its origins but also whether it was deliberate or accidental. That is, it leaves doubts about whether or not it was sabotage at all.

Response: By contrast, if you use Internet Noise, the random browsing that results might be identifiable as such, in which case it may simply be excluded from the data set. This may help you escape the training regime, but it won’t smuggle misinformation into the system to disrupt that regime for others. If, however, the random browsing was not random but generated according to subtly misleading patterns—like the imperceptible noise in the perturbed images—then the misinformation may make its way into the training data. And if there is enough of it, it will poison the model as a whole.


Prompt: Protracted People’s War Research in adversarial machine learning typically falls under the rubric of cybersecurity, so it has some similarities to the field of cryptography. The success of cryptography depends on difficult mathematical operations, such as factoring prime numbers. These operations are relatively easy for individuals or small organizations to take advantage of, and mostly resilient against adversaries with more resources. The marvel of cryptography is that it is a technology that runs against the typical gradients of power. It favors the less powerful against the more powerful.

Response: Perhaps something similar is at work with adversarial machine learning, favoring the resister against the machine. To quote Ian Goodfellow and Nicolas Papernot, two leading researchers in the field: On the theoretical side, no one yet knows whether defending against adversarial examples is a theoretically hopeless endeavor ... or if an optimal strategy would give the defender the upper ground.... On the applied side, no one has yet designed a truly powerful defense algorithm that can resist a wide variety of adversarial example attack algorithms.


Prompt: Like cryptography, the goal of such disruption is not necessarily a single act of sabotage, but rather a distributed undertaking that decreases the cost-effectiveness of data collection at scale. In the case of cryptography, the more people who use encrypted communication applications like Signal, the more expensive state surveillance becomes—hopefully to the point where it becomes infeasible. Similarly, the less accurate these machine learning systems become due to user disruption, the more costly they are, and the less likely they are to be relied upon.

Response: While adversarial machine learning is still in its early years, perhaps it will provide a tactic to complement the parallel struggles on the policy level. We are under no obligation to honestly train these models. If regimes of algorithmic discipline are implemented without our consent, we will need a toolbox of resistance that can directly disrupt their effectiveness.


Prompt: Are you anxious about the role of algorithms in your life? Concerned about the disappearance of human judgment and its replacement by machines? Horrified by the movie Minority Report’s depiction of a world where people can be sentenced based on crimes they have not yet committed? If so, reading the recent coverage of sentencing algorithms in the criminal justice system may be just what you need to confirm your worst fears.

Response: Over the past year or so, numerous media and academic articles have tackled the subject of “risk-assessment tools.” These are software programs used by criminal courts to quantify defendants’ risk of committing another crime based on variables relating to their criminal history, such as criminal record and type of offense, and socio-demographic characteristics, like age, gender, and employment status. The programs produce a “score” for each defendant, often ranging from one to ten, which is supposed to indicate the likelihood of recidivism.


Prompt: Risk-assessment tools are rapidly proliferating, subjecting an increasing number of defendants to their rule. According to recent estimates, more than sixty risk-assessment tools are currently being used in the United States. From pretrial justice to probation, parole, sentencing, juvenile justice, sex offenses, and drug-related offenses, predictive tools now permeate almost all parts of the criminal justice system. They are also spreading across borders: risk-assessment instruments are being developed and licensed in Canada, Australia, and several European countries.

Response: There is little doubt that these tools discriminate against African Americans. As a 2016 ProPublica investigation revealed, COMPAS—a predictive instrument used for bail and sentencing decisions across the United States—gives harsher risk scores to African Americans compared to whites. In their statistical analysis, the journalists found that among defendants who ultimately did not reoffend, blacks were more than twice as likely as whites to be classified as high or medium risk.


Prompt: Why? The mechanism is simple. Since predictive algorithms draw on historical data to train their models, and since historically the US criminal justice system has arrested, convicted, and incarcerated African Americans at higher rates compared to whites, risk-assessment tools reproduce discriminatory patterns. There is no easy fix for these structural issues. Even if one changes the variables included in the models, risk-assessment tools will continue to have different rates of false positives across racial lines, so long as we want scores to mean the same thing in terms of risk for blacks and whites, simply because of the distribution of each group’s data.

Response: Predictive algorithms are also secretive, which makes them particularly ominous in the criminal justice context. The companies that build them often refuse to share the training data and code used in their products. This is the case for the COMPAS tool analyzed by ProPublica, which was built by Northpointe, a for-profit company. Although Northpointe challenged ProPublica’s analysis, they did not share their code or data, arguing that it was proprietary. Through public records requests, the ProPublica team was able to collect risk scores for thousands of criminal defendants. Yet this process was expensive and time-consuming. Most people simply do not have the resources for it.


Prompt: Similarly, within jurisdictions that use risk-assessment tools, defendants and defense attorneys often do not know their risk score—or even, for that matter, whether they have one. They have no option to appeal or contest their score. In other words, defendants are sentenced based on factors they do not know and cannot dispute—a situation not unlike K.’s in Kafka’s The Trial.

Response: Algorithms in The Wild The rapid growth of risk-assessment tools in criminal justice sounds like an algorithmic nightmare come true. It is no surprise that dystopian references such as Minority Report or The Trial come to mind. Risk-assessment tools resonate with our worst anxieties about algorithms and automation in the digital age. They capture the dark side of our imagination about technological change.


Prompt: Such critiques are important and much needed. Yet they also miss important points. In particular, existing coverage overwhelmingly focuses on the tools themselves, their models, and their construction methods. In contrast, very few studies explore how these technologies are used in criminal courts. This is where my own research comes in. Over the past year and a half, I have conducted ethnographic fieldwork in criminal courts in several locations in the United States. I went to hearings, listened to plea bargaining negotiations, and interviewed judges, prosecutors, and pretrial and probation officers. I observed legal professionals in their daily work, trying to see how, when, and why they used risk scores to make decisions.

Response: Based on this research, two important qualifications to the dominant narrative emerge. First, it is still unclear whether risk-assessment tools actually have a great impact on the daily proceedings in courtrooms. During my days of observation, I found that risk-assessment tools are often actively resisted in criminal courts. Most judges and prosecutors do not trust the algorithms. They do not know the companies they come from, they do not understand their methods, and they often find them useless. Consequently, risk-assessment tools often go unused: social workers complete the software programs’ questionnaires, print out the score sheets, add them to the defendants’ files… after which the scores seem to disappear and are rarely mentioned during hearings or plea bargaining negotiations.


Prompt: Of course, these findings are far from representative of all criminal courts in the United States. There are certainly criminal courts where risk scores play a more important role. Yet this indicates that the nightmarish, Minority Report-inspired descriptions of automated justice in the algorithmic age may not be entirely accurate. Courts are just messier than that.

Response: Paperless Justice The time I spent in criminal courts also made me realize that, for many judges, prosecutors, and court administrators, the technological issue of the day is not so much risk-assessment tools as the transition to paperless case-management systems. Until recently, courts have largely operated in a paper-based world. Thousands of paper files are still being carted around by clerks and administrators in most criminal courts, while prosecutors and defense attorneys overwhelmingly interact through scribbled paper notes.


Prompt: All of this is currently being transformed through the development of complex, large-scale case-management systems. These systems encompass a wide range of functions. Prosecutors and defense attorneys can upload supporting document and file paperwork. Court administrators can schedule hearings and process criminal cases online. Judges and clerks can save and record sentencing decisions. Pretrial and probation officers can access the files of defendants and convicts with a few clicks.

Response: Like any other digital infrastructure, these case-management systems register, compile, and store large amounts of data in a single place. The question then becomes: what should courts do with that data? To date, most courts don’t do anything with it, because they don’t have the resources to hire computer scientists. A few courts use it to build predictive analytics units, integrating data and drawing on resources from prosecutorial and police departments to help predict future offenses. Other courts are adopting a more reflexive approach, trying to shed light on their internal functioning with the goal of reforming the least efficient parts of their administration.


Prompt: We need to pay much closer attention to how courts use these new troves of digital data. They may take a more dystopian direction, building up discriminatory dragnets through the creation of new predictive categories of “risky” people. These dragnets would be similar to what risk-assessment tools already do in their limited way, but with access to more encompassing and up-to-date data, as well as actionable resources to target and prosecute the individuals identified by predictive technologies.

Response: Or courts may choose to use these digital systems to gather actionable analytics for social justice. There are several ways in which data could be tremendously helpful for criminal justice reform. It could identify and expand procedures that successfully reintegrate former prisoners into society. It could also help limit prosecutorial discretion in a system where the vast majority of cases are settled by plea bargain rather than a trial. Data may even be used to incentivize indigent defense attorneys to provide better outcomes for their clients, or to identify criminal procedures with high rates of incarceration in order to reduce them.


Prompt: Using digital data to achieve these goals will take time and effort, of course. It will likely emerge from jurisdictions themselves, as they collect and analyze data to understand what they do well and where they fail—not from for-profit vendors that provide questionable off-the-shelf solutions. Sometimes tech criticism shares more in common with tech evangelism than first meets the eye. Both have a strong tendency to disregard contextual, political, and institutional factors. We shouldn’t merely invert the Silicon Valley mantra that technology provides the solution for every problem, to arrive at the argument that technology can’t solve any problem.

Response: Instead, we need to acknowledge that technology alone doesn’t always mean much. Rather than focusing on the internal mechanics of risk-assessment tools, we should pay closer attention to the contexts in which those tools are developed and deployed. Politics, not technology, is the force responsible for creating a highly punitive criminal justice system. And transforming that system is ultimately a political task, not a technical one. Yet technology can play an important role. With enough political power, the algorithms that help sustain mass incarceration could be repurposed into tools that help dismantle it.


Prompt: Before the first drone came, the most unusual thing the birds of South Rupununi would glimpse above the canopy could have been mistaken for a lone, tough fruit. It was a transient thing: bobbing up, then pausing, then dipping back into the forest, only to rise again elsewhere at irregular intervals. You’d forgive the birds if they were confused.

Response: To the people below, though, the object was a GPS unit tied to a pole; and if you were to follow the pole down past the canopy, almost to the forest floor where service was sparse, you’d find a hand hoisting it up. Not long before the drone came, this was how the cartographers would stitch together their maps: pin-drop by pin-drop, with smartphones and GPS units, geo-referencing the data with satellite imagery.


Prompt: But the drone would come. And before launching it into the sky, the cartographers who built it would name it for a bird, and in a little patch of southwest Guyana near the Brazilian border, the kowadad—osprey—would begin to sketch its own maps. Drone Vision At the mobile ground-control unit near the mixed Makushi-Wapichan indigenous community of Shulinab Village, you might find Nicholas Fredericks planning a flight path. Fredericks, a former cowboy turned Wapichan community leader, is here because the drone’s spatial resolution affords a degree of photographic detail that far outpaces that of satellite data. That is, Kowadad has sharper eyes than the Guyanese government—which means Fredericks can present real-time evidence to the state, at a higher quality than that to which it has access, when illegal loggers and miners hurdle over their concession boundaries and infringe upon Wapichan forest lands.

Response: In theory, it’s the kind of evidence that ought to be incontrovertible. But political movement on Amerindian land-use claims is often sluggish at best (if not wholly resistant). Over the past decade, a federation of Wapichan communities called the South Central People’s Development Association (SCPDA)—where Fredericks is a project coordinator—has worked to piece together high-resolution maps of the seventeen communities’ territories. Mapping is a means to establish land claims on an ontological footing equal to the government’s. The drone, which resembles an exaggerated model airplane and was assembled via YouTube and Skype tutorials with the onsite help of the Oakland-based nonprofit Digital Democracy, is the latest tool to aid the efforts.


Prompt: In late 2015, Fredericks traveled to the Paris Climate Conference to accept a prize from the United Nations on behalf of the SCPDA, its ongoing mapping initiatives, and an accompanying land-use plan, which argues for secure indigenous land rights and the role of traditional knowledge in protecting the forests and their resources. “You have already listened, learned, and understood from us that our traditional practices are sustainable,” he told a packed Parisian auditorium upon receiving the Equator Prize. “We now need our governments to recognize this, too.” That’s an ongoing challenge, explained Fredericks when I met him at the climate talks in Paris. “Some of the advantages of the work are that it allows us to build capacity with respect to technology and it enables us to present visual evidence—accurate locations of destructive sites—to the government and to the Ministry of Environment,” he said.

Response: In some cases, the Wapichan mappers have aided the Guyana Defence Force by supplying GPS pin-drops of live, far-flung, illegal border-crossings made by Brazilian gold miners. The crossings were subsequently blocked by the military. “We got their attention,” said Fredericks, referring to state actors like the Defence Force and the Guyana Forestry Commission. But attention does not always translate to recognition, and Fredericks is often only able to secure verbal agreements related to Amerindian land claims.


Prompt: Mapping Against Capital For the state, the forest is strategic. It is either to be conserved or exploited (but never lived in). Decisions must be made about the relative merits of biodiversity and resource extraction and tourism. Perhaps a forest is valuable to the state because it can be logged for timber. Perhaps a forest is most valuable not as a forest at all, but clear-cut and replaced with a rubber plantation, a palm oil plantation, or a cattle ranch. Parcels must be divvied. And to align with the logic of private property, these decisions must be mapped. For communities residing in and around forests—who may be quite literally written out of the equation—the map simultaneously speaks and silences.

Response: The erasure of community by mapping is a reminder of the enterprise’s inherently sticky subjectivity. Consider the well-known case of the Mercator projection—that canonical flat thing that pops up when you Google “world map”—in which irregularly spaced latitude lines balloon the perceived area of land farther away from the equator. Europe expands; Africa shrinks. But this subjectivity isn’t just a function of subtleties like choice of geometric projection. To take one broad example, the entire notion of the territorial nation state (much less that of private property) is entirely incompatible with many traditional conceptions of place.


Prompt: Twenty years ago, a young scholar named Nancy Peluso used the term “counter-mapping” to refer to acts of non-state cartography. Since at least the sixteenth century, elites have used maps to demarcate resource rights. “However,” wrote Peluso, “if maps can be seen as one of many ‘authoritative resources’ that states mobilize to consolidate their own power, then local groups’ appropriation of the technology of mapping may help to counterbalance or at least offset the previous monopoly of authoritative resources by the state or capital.” Today, NGOs like Digital Democracy appear to have taken up the mantle, arguing for a technological democratization of mapping. And while the idea does contain radical potential, the forces that have made it feasible are distinctly unradical. As human geographer Agnieszka Leszczynski suggests, it was precisely a neoliberal shift in the political economy of mapping that has helped enable its democratization.

Response: Consider a largely deregulated tech giant like Google releasing a privatized mapping service—previously the role of a public-sector mapping organization—and subsequently opening the Google Maps API to the public. Anyone can hook into the API (or, better yet, open-source mapping services like OpenStreetMap) and achieve a base level of technological legitimacy. As the welfare state recedes, cartographic responsibility shifts. It is a small irony that the neoliberal hollowing of the state and its twin cults of individualism and privatization would pave the road for decentralized counter-cartographies.


Prompt: Peluso’s case study focused on Kalimantan in Indonesia, where the Food and Agriculture Organization of the UN had found that Japanese, Philippine, US, and European timber concessions had been allocated 57.9 million hectares of forest by the government—despite the fact that only 43.3 million hectares were classified for timber production. The governmental forest-planning map that guided the awarding of concessions also plainly ignored indigenous community boundaries.

Response: This imbalance between production, conservation, and community remains just as exaggerated today. Overlapping permits have led to a situation in which “130 percent of the total area of West Kalimantan is now covered by concessions for mining, palm oil, logging, and pulp and paper plantations,” according to a 2017 study by Indonesian scholar-activist Irendra “Radja” Radjawali and his colleagues.


Prompt: No Magic Wands Radja’s work with Dayak communities in West Kalimantan mirrors Wapichan efforts in South Rupununi. He too assembled a community drone via YouTube tutorials for less than six hundred dollars. “The government, the military, the companies—they all use maps,” he explained, speaking shortly after Fredericks back in Paris. “We have to counter-map. We have to use the very same technology to fight back.” In the Indonesian case, counter-hegemonic mapping has sometimes yielded more tangible results than in Guyana. Recently, Radja and his team used the community drone to produce a high-resolution map of a bauxite mining concession that overlapped with a local community’s land claims in the sub-district of Tayan Hilir. The mining company in question had drained a lake, which had previously been vital for subsistence fishing. By geometrically correcting the aerial photographs to produce so-called “orthophotos” and then overlaying the images on the official concession map, the researchers could prove the company was illegally operating outside its concession boundary. Since the publication of the orthophotos in a 2016 report, the Tayan mining operation has been shut down.

Response: Perhaps more importantly, in a case heard before the Constitutional Court of Indonesia—unrelated to the question of concession boundaries per se—drone orthophotos of West Kalimantan were accepted as supporting evidence of detrimental environmental effects of mining in the region. When the court ruled against the mining corporations in the case in question, environmental activists and civil society organizations celebrated it as having set a precedent for the evidentiary use of counter-maps in the legal system.


Prompt: In Guyana, Fredericks contends that his central cause is the preservation of traditional knowledge. When we last spoke, he reflected on how it was the proliferation of cell phones among the young people in Shulinab Village that had originally motivated him to seek out ways “to use technology to our benefit”—that is, not as another vector of Western influence but as a means of upholding and elevating indigenous practices.

Response: It’s a quiet little paradox, and one that suggests Amerindian and other indigenous cartographers are often fighting two battles at once: one on the legal front, in an effort to ensure community mapping techniques count as admissible evidence in court; the other epistemic, in which indigenous ontologies square off against Western (colonial) mapping practices.


Prompt: The latter case is often fraught with compromise. Fixing borders cartographically, for example, may threaten to cement something traditionally conceptualized as fluid or shared. Take, for example, software developer Victor Temprano’s efforts to crowdsource and superimpose Native and First Nations’ territory on a map of North America at Native-Land.ca. To settler eyes, the result is forceful and reorienting. But these territorial boundaries were often never written down as such. At risk of generalizing across diverse systems of indigenous land tenure: they were delineated orally or ecologically or seasonally or cyclically—or, in the case of communal ownership, they didn’t really exist at all.

Response: Fixing borders can not only be misleading, but dangerous. The act of accepting a given state’s administrative units can in turn be co-opted by the state for further land-grabs. In West Kalimantan, for example, some researchers have argued that counter-mapping Dayak villages—which drew boundaries to land that hadn’t yet been surveyed by the government—has had the unintended consequence of delegitimizing other Dayak land claims, as well as bringing indigenous institutions under state control. As cultural geographers Joel Wainwright and Joe Bryan noted in 2009, a valid concern arises that counter-mapping strategies “do not reverse colonial social relations so much as they rework them.” Radja and his colleagues are cautious, too. “Rather unsurprisingly,” they write, “drones are not a magic wand that can conjure away hierarchies and power structures at the local level or in wider society.” They note that although drone workshops in Dayak communities have led to more young people taking up activist and leadership positions, almost all of these people were male. What drones can do for sovereignty and land tenure they can perhaps not do for gender equality. Some of the revolutionary potential here will mature slower than the rest, or perhaps not at all.


Prompt: Which is indeed what one would expect. “The effect of a concept-driven revolution is to explain old things in new ways,” wrote the physicist Freeman Dyson in Imagined Worlds. “The effect of a tool-driven revolution is to discover new things that have to be explained.” Yes, the counter-cartographers have much to explain. First, they need the powerful to listen.

Response: The United States has the largest prison population in the world: over 2 million people are incarcerated in federal and state prisons, with an additional 4.7 million people on probation or parole. This prison population is disproportionately black and brown—according to the Sentencing Project, black people are incarcerated at a rate five times higher than white people, and Latinx people are incarcerated at a rate 1.4 times higher than white people.


Prompt: As prison populations have expanded and costs have increased, policy wonks with many different political viewpoints have looked to alternatives, including monitoring people electronically instead of imprisoning them. In The Atlantic, Graeme Wood praises this approach for presenting “a revolutionary possibility: that we might turn the conventional prison system inside out for a substantial number of inmates, doing away with the current, expensive array of guards and cells and fences.” “The potential upside,” he writes, “is enormous.” Commonly deployed in the form of an ankle bracelet, electronic monitors are not just supposed to lower costs, but also reduce prison overcrowding, decrease parole violations, and lower recidivism rates. Most of the academic work on electronic monitors focuses on these issues. Are people released from prison and tracked electronically less likely to reoffend? Does monitoring reduce crime? But as these questions occupy the minds of scholars, the reality of life with an electronic shackle seldom enters the narrative. The truth is that electronic monitors increase the impact of the criminal justice system rather than dampen it. And as the technology outpaces our critical reckoning with the surveillance apparatus that we are building in place of prisons, it will likely only get worse.

Response: 21st Century Shackles The primary form of electronic monitoring used in the United States is ankle bracelets that are worn continuously and used to track the location of the people wearing them. The devices track a user’s location using radio frequencies (RF) or the global positioning system (GPS), which relies on satellites. In 2015, more than 125,000 people were supervised using these devices.


Prompt: RF devices generally don’t pinpoint a person’s location. Instead, they only monitor whether or not the wearer is at home, and are used for “house arrest.” (Usually, a person on house arrest may only leave their home for a set of prescribed activities, like going to work or attending religious services.) They connect to a base station that is plugged in at home, and link to the internet via a landline or a dedicated cellular unit.

Response: By contrast, GPS monitors track location based on satellite information. As a result, they can alert parole officers and other officials if the wearer enters a restricted area, such as a school zone. GPS systems tend to lead to less restrictive rules, but also share more information with the police. Some GPS ankle monitors have transmission built-in; others require that a dedicated cell phone or other transmitter be carried at all times. For all types of devices, police are alerted if the device strap is cut or if the sensors detect other types of tampering.


Prompt: Ankle monitors are usually bulky. As a result, they can cause both physical and psychological discomfort. Even one of the newest types of monitors, BI Incorporated’s LOC8, weighs in at 6 ounces and is 4.125 inches wide, 2.5 inches tall, and 1.25 inches in depth. For comparison, an Apple Watch is 3.6 ounces, 1.4 inches wide, 1.6 inches tall, and 0.44 inches deep.

Response: Many of those who have worn ankle monitors refer to them as their “shackles” and feel enslaved by the device. Wearers report the feeling of being watched, and have found the psychological discomfort even more profound than the physical. Some also experience stigma—because of the size of the devices, they can be obvious to casual viewers, endangering wearers’ employment prospects and potentially resulting in street harassment.


Prompt: Virtually everyone who has worn an ankle monitor has had a technological issue with their device, compounding the psychological stress. Battery life is a constant concern. Many parole officers believe that wearers let their monitors die in order to travel more freely, so they view dead batteries with suspicion. Thus wearers are rightfully concerned that if their monitor batteries die they can be thrown in jail or sent back to prison. Having to charge a device for two hours at a time via a two-foot charging cable only exacerbates the feeling of being chained. Constantly having to find access to power can cause significant anxiety.

Response: There is also a literal cost to being monitored. Individuals wearing ankle monitors are often required to pay a $5 to $20 fee per day of use, despite the fact that the retail price for a GPS monitor is only between $150 and $250. LCA, the primary provider of electronic monitoring technology for the state of the California, calls these programs “self-funding,” and claims they are “typically provided at no cost to the county.” When the monitors are used on juveniles, parents and family members end up paying the costs. Some counties in California have opted to create indigent funds for those truly unable to pay for the monitoring—but in at least one county, the determination of whether a wearer has the ability to pay is made by the monitoring company itself.


Prompt: Scope Creep Given this history, it seems likely that electronic monitoring will continue to become more popular, especially with the advent of new technological capabilities. The state-of-the-art LOC8 device introduces an accelerometer, which measures the user’s movement. This means it can now function as a tracking device in the same way as a Fitbit does—only users are legally required to wear it. Parole officers could know when the wearer is sleeping, moving, and more, all using acceleration data.

Response: As the technology improves, an important question technologists must ask is whether we should build these tools, not whether we can. For most wearers of electronic monitors, being out in the world is better than being in prison, even if one is under surveillance. But like many techno-utopian solutions, the reality of electronic monitoring as an alternative to prison is far removed from the fantasy. The user experience of ankle monitors lags behind even basic consumer tracking products, like the Fitbit or the Apple Watch. Although companies call the people who wear the monitors their “clients,” their real customers are police departments and prison systems. The website of BI Incorporated, an electronic monitoring provider, highlights their “easy-to-use mapping features” and twenty-four-hour call centers. The priority is selling government officials on the product—not the comfort and quality of life of the person wearing the device.


Prompt: Second, as the activist and scholar James Kilgore has argued, electronic monitoring allows law enforcement officers to impose additional conditions on parole and probation, creating new possibilities for arbitrary punishments. In his 2015 report “Electronic Monitoring is Not the Answer,” Kilgore interviewed Kent Schultz, who was forced by his parole officer to run back into his burning apartment to retrieve the base station for his ankle bracelet. Despite saving the base station, he was thrown in jail twenty-four hours later. A warrant had been automatically generated for his arrest.

Response: The faults in the technology can compound these problems. Kilgore recalls that when he himself was on a monitor, he was forced by a poor GPS connection to stand in his front yard at 2 AM during an Illinois winter so that his device could reconnect. Most jurisdictions do not have firm rules for what can and cannot be required of a person on a monitor, as police claim the person consents to the surveillance. The question of whether that consent is meaningful when the alternative is imprisonment is usually ignored.


Prompt: There is also what scholars Catherine Crump and Christina Koningisor call the “net-widening problem.” This is the fact that the convenience and low cost of electronic monitoring to police departments might result in entirely new populations being placed under surveillance—not just as a substitute for incarceration, but as an expansion of the criminal justice system.

Response: As surveillance technologies get cheaper—and as those costs are passed from police departments to users—the incentives to deploy electronic monitoring grow stronger. Even low-risk offenders who would have been let out without cash bail might be targeted for monitoring. The technology was originally intended for parole and probation, but is now being widely used to ensure people with pending immigration cases and asylum seekers show up in court.


Prompt: Once these devices are widespread, it becomes easier to justify accessing the data for prosecutorial, rather than rehabilitative, purposes. The California Department of Corrections and Rehabilitation claims that “GPS has proven to be an effective tool used in supervising offenders who are at high risk of re-offending and where knowledge of their whereabouts is a high priority for maintaining public safety.” What that means in practice is that police within the United States routinely access ankle monitor GPS data to see if wearers were near crime scenes, without the legal process that would be required to gain that information from a third party.

Response: Sometimes such location information is gathered for the express purpose of prosecuting new crimes, unrelated to any rehabilitative purpose. In a recent case out of New York, a judge finally pushed back. Judge Jack Weinstein ordered the suppression of all evidence gathered through an ankle monitor. He found that law enforcement had required a parolee to wear the monitor for an additional two years to gather information for a pending criminal indictment, and that was an unconstitutional violation of the parolee’s rights.


Prompt: This scope creep means that electronic monitoring tends to increase the punitive effects of the criminal justice system rather than decrease them. Although initially billed as a way of keeping people out of jail, monitors get used on people who would never be in jail anyway. Electronic surveillance technologies and data aggregation, built without technological checks and balances, empower fewer police officers to monitor more people—and to punish those who do not comply. At every turn, the design of these systems prioritize police over wearers. The companies that sell them have little incentive to do otherwise.

Response: But it isn’t just the technology. Many of the problems with electronic monitoring are problems with the criminal justice system more generally. Like many technologies, electronic monitoring is accelerating the rate of change. As deployment outpaces the development of new policy norms or rights frameworks, it amplifies existing disparities. Surveillance disproportionately affects black and Latinx communities because the criminal justice system disproportionately affects these communities. Building the future we want will require not only imagining different ways to use technology, but winning an argument about whether punishment or rehabilitation are better goals.


Prompt: “You good?” a man asked two narcotics detectives late in the summer of 2015. The detectives had just finished an undercover drug deal in Brentwood, a predominately black neighborhood in Jacksonville, Florida that is among the poorest in the country, when the man unexpectedly approached them. One of the detectives responded that he was looking for $50 worth of “hard”—slang for crack cocaine. The man disappeared into a nearby apartment and came back out to fulfill the detective’s request, swapping the drugs for money.

Response: “You see me around, my name is Midnight,” the dealer said as he left, not realizing who he had just met. Before Midnight departed, one of the detectives was able to take several photos of him, discreetly snapping pictures with his phone held to his ear as though he were taking a call. That’s at least allegedly what happened, according to case details from The Florida-Times Union and a court motion. Details get murkier from there.


Prompt: Two weeks later, police wanted to make the arrest, but didn’t know who they had bought the crack from. The only information they had were the smartphone pictures, the address where the exchange had taken place, and the nickname “Midnight.” Stumped, the Jacksonville Sheriff’s Office turned to a new tool to help them track down the dealer: facial recognition software.

Response: The technology helped them pin down a suspect named Willie Lynch. Lynch, who has been described by close observers of the case like Georgetown University researcher Clare Garvie as a “highly intelligent, highly motivated individual” despite only having graduated high school—he even filed his own case motions, which could be mistaken for ones written by an actual lawyer—was eventually convicted and sentenced to eight years in prison. He is now appealing his conviction.


Prompt: Whether or not Willie Lynch is “Midnight” remains to be seen. At the very least, his conviction deserves closer scrutiny. Many experts see the facial recognition technology used against him as flawed, especially against black individuals. Moreover, the way the Jacksonville Sheriff’s Office used the technology—as the basis for identifying and arresting Lynch, not as one component of a case supported by firmer evidence—makes his conviction even more questionable.

Response: The methods used to convict Lynch weren’t made clear during his court case. The Jacksonville Sheriff’s Office initially didn’t even disclose that they had used facial recognition software. Instead, they claimed to have used a mugshot database to identify Lynch on the basis of a single photo that the detectives had taken the night of the exchange. Even after the office’s use of the technology became apparent, they still gave few answers about how they used the technology or how they trained Celbrica Tenah, the analyst in the Sheriff’s Office Crime Analysis Unit who identified Lynch using facial recognition software.


Prompt: An Imperfect Biometric The lack of answers the Jacksonville Sheriff’s Office have provided in Lynch’s case is representative of the lack of answers to questions that facial recognition poses across the country. “It’s considered an imperfect biometric,” said Garvie, who created a study at Georgetown called The Perpetual Line-Up on facial recognition software. “There’s no consensus in the scientific community that it provides a positive identification of somebody.” The software, which has taken an expanding role among law enforcement agencies in the U.S. over the last several years, has been mired in controversy because of its effect on people of color. Experts fear that the new technology may actually be hurting the communities the police claims they are trying to protect.

Response: “If you’re black, you’re more likely to be subjected to this technology and the technology is more likely to be wrong,” House Oversight Committee ranking member Elijah Cummings said in a Congressional hearing on law enforcement’s use of facial recognition software in March 2017. “That’s a hell of a combination.” Cummings was referring to studies like the one published in 2016 by the Center on Privacy and Technology at Georgetown Law. This report found that black individuals, as with so many aspects of the justice system, were the most likely to be scrutinized by facial recognition software in cases. It also suggested that software was most likely to be incorrect when used on black individuals—a finding corroborated by the FBI’s own research. This combination, which is making Lynch’s and other black Americans’ lives excruciatingly difficult, is born from another race issue that has become a subject of national discourse: the lack of diversity in the technology sector.


Prompt: Racialized Code Experts like Joy Buolamwini, a researcher at the MIT Media Lab, think that facial recognition software has problems recognizing black faces because its algorithms are usually written by white engineers who dominate the technology sector. These engineers build on pre-existing code libraries, typically written by other white engineers.

Response: As the coder constructs the algorithms, they focus on facial features that may be more visible in one race, but not another. These considerations can stem from previous research on facial recognition techniques and practices, which may have its own biases, or the engineer’s own experiences and understanding. The code that results is geared to focus on white faces, and mostly tested on white subjects.


Prompt: 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.

Response: “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.


Prompt: Unregulated Algorithms Even as the use of facial recognition software increases in law enforcement agencies across the country, the deeper analysis that experts are demanding isn’t happening. Law enforcement agencies often don’t review their software to check for baked-in racial bias—and there aren’t laws or regulations forcing them to. In some cases, like Lynch’s, law enforcement agencies are even obscuring the fact that they’re using such software. To take another example, in their Perpetual Line-Up study Georgetown researchers found that the Pinellas County Sheriff’s Office in Florida runs 8,000 monthly facial recognition searches, but the county public defender’s office said that police have not disclosed the use of the technology in Brady disclosure—evidence that, if favorable to the defense, must be provided to them by prosecutors.

Response: Garvie said she is confident that police are using facial recognition software more than they let on, which she referred to as “evidence laundering.” This is problematic because it obscures just how much of a role facial recognition software plays in law enforcement. Both legal advocates and facial recognition software companies themselves say that the technology should only supply a portion of the case—not evidence that can lead to an arrest.


Prompt: “Upon review, all facial recognition matches should be treated no differently than someone calling in a possible lead from a dedicated tip line,” writes Roger Rodriguez, an employee at facial recognition vendor Vigilant Solutions, in a post defending the software. “The onus still falls on the investigator in an agency to independently establish probable cause to effect an arrest,” he continues—probable cause that “must be met by other investigatory means.” It doesn’t always work out that way, of course.

Response: Even if facial recognition software is used correctly, however, the technology has significant underlying flaws. The firms creating the software are not held to specific requirements for racial bias, and in many cases, they don’t even test for them. “There is no independent testing regime for racially biased error rates,” Georgetown researchers wrote in their Perpetual Line-Up report. “In interviews, two major face recognition companies admitted that they did not run these tests internally, either.” A third I spoke to, CyberExtruder, a facial recognition technology company that markets itself to law enforcement, also said that they had not performed testing or research on bias in their software. Another, Vigilant Solutions, declined to say whether or not they tested for it. CyberExtruder did note that certain skin colors are simply harder for the software to handle given current limitations of the technology. “Just as individuals with very dark skin are hard to identify with high significance via facial recognition, individuals with very pale skin are the same,” said Blake Senftner, a senior software engineer at CyberExtruder.


Prompt: “[Race is] just very hard to control for in their testing,” Garvie explained. “There haven’t been enough public studies, but the limited research that has been done does suggest that the algorithms may have different accuracy rates depending on the race of the subject.” Garvie and her colleagues believe that NIST is well positioned to help with such studies. The agency, part of the U.S. Department of Commerce, conducts voluntary tests with facial recognition companies every four years and is testing for variances in results by country of origin—which Garvie notes “can be a good proxy for race.” NIST’s tests won’t come soon enough for Lynch, however. His case is currently playing out in the Florida First District Court of Appeal. The clock also can’t be turned back for others like him, who may have been unfairly tried as a result of less than perfect software without transparent standards for its use.

Response: Facial recognition software raises many questions that need clear answers. Obtaining those answers will take more than commissioning studies, as vital as they are. It’s also essential that laws catch up with the technology, in order to provide people like Lynch with the opportunity to know the tools that are being used against them. Most importantly, we need to take a closer look at who’s making these algorithms—and how they’re doing it.


Prompt: 1. “Being so many different sizes in a day is very confusing,” Alice tells the Caterpillar. Just think how the internet feels! It’s big enough to make the world feel small, and small enough to fit in the palm of your hand. It dissolves huge chunks of data into particles of light, then flings them across filaments of optical fiber roughly the width of human hair.

Response: Those filaments link continents—and the most intimate regions of our lives. What else runs under the Pacific Ocean and also keeps you company on the toilet? The internet’s vastly different scales don’t just coexist. They coproduce each other. The planetary and the personal, the tiny and the titanic, are locked, circling, in a spiral dance. Technology shrinks to grow, and grows to shrink.


Prompt: This isn’t the first time technology has rescaled the world. In the 1860s, when Lewis Carroll was writing Alice in Wonderland, new railway and telegraph networks were reshaping time and space. Journeys that used to take days now took hours. Letters that used to take weeks to arrive now took minutes.

Response: A web of wire and steel pulled whole continents together. “We have seen the power of steam suddenly dry up the great Atlantic Ocean to less than half its breadth,” one author marveled. “The Mediterranean… has shrunk into a lake.” 2. Moore’s Law says you can fit twice as many transistors on a chip about every two years. As the chips get smaller, our capacity to store and process information gets bigger. Computing becomes ubiquitous, digitization universal. The fountains of the great deep are broken up, and the face of the earth is covered with data.


Prompt: Miniaturization enables and demands new maximizations. Billions of connected devices require the construction of vast data centers. A startup with thirteen employees acquires millions of users within months—and gets bought for $1 billion. A corner of Northern California formerly occupied by orchards and canneries becomes shorthand for the future. Software eats the world.

Response: Software doesn’t actually eat anything, of course. The phrase is pure fetishism: relations between people reimagined as relations between things. Somebody is always doing the eating—and somebody is always getting eaten. A cafeteria worker who helps reproduce the labor power of LinkedIn’s engineers with quinoa and kale shares a one-bedroom apartment with a dozen family members. A media worker whose industry is being liquidated by Facebook watches her income dwindle as she cranks out content she hopes will go viral.


Prompt: There is no limit to the scale of wealth and power that a few men are permitted to accumulate. Meanwhile, the rest of us are asked to live smaller, leaner, more flexibly; to enterpreneurialize ourselves into serfdom—”to eat a coffee for lunch,” as the infamous Fiverr ad puts it—to lower our expectations, and in some cases even our life expectancy, to adapt to a Lilliputian existence at the feet of our new kings.

Response: 3. This issue looks at how scale shapes technology and the tech industry—and how their bigness and smallness are reshaping us. Our writers explore why technologists come under pressure to think big and think small—to scale and to compartmentalize. VCs demand massive growth on a short timetable. Because the vast majority of the startups they fund will fail, the few that succeed must succeed hugely. At the same time, software itself is modular. Engineers organize code into abstractions that help them manage, and conceal, complexity. The “black boxes” that result can be good for business, and bad for humans.