Prompt: It also sounds like you’re trying to develop a model of militant research; that is, research that is rooted in a set of political commitments and organizing practices. It’s a model of research where the ideas emerge through struggle, through practice, through social movement. That’s a different approach than the standard scholarly one.

Response: The scholarly model also operates at a different scale. It’s more interested in creating concepts that govern because they speak to multiple communities at once. But our work has to happen at a different scale because our inquiry is accountable to specific people. It’s inquiry that’s conducted through caring about particular individuals. Someone comes to you and says, "I don't understand what's going on. I don't understand why I got fired, why my husband didn’t get the job, why my brother's parole was denied." And you start to begin to answer those questions, which are very personal.  Our work happens at this scale. The scale of friends, family, and loved ones. And yes, the answers often point to the role of giant sociotechnical systems. But we’re answering individual questions. And we’re doing it because we care about the people we are in community with, not because we’re trying to develop the best idea to sell a book. Our intervention is effective when we’re able to find the knowledge that allows people to enact meaningful change in their lives.


Prompt: Abolitionist Futures You mentioned earlier that your goal is to abolish these systems, not reform them. What does an abolitionist campaign against a carceral technology look like? I’m working on a campaign right now in Portland to ban both the private and public use of facial recognition technology. A handful of cities have banned the use of facial recognition by local government entities like police departments, but private businesses have been unaffected. The Portland ban would extend to the private sector too.

Response: It's been controversial because a lot of people who are civil rights oriented have been worried that you're infringing on an individual's ability to use this technology if they want to. But if you're organizing from an abolitionist perspective, you recognize that the private rollout of this technology is still a carceral technology. These technologies never exist without their carceral counterpart. Take the introduction of face-scanning software to unlock people’s phones. Industry rolls out these artifacts of private consumption that normalize the existence of these technologies—technologies that have always been entangled in carceral systems.  We recognize facial recognition technology, a weapon used by law enforcement to identify, profile, and enact violence against categories of people. So individuals opting in to unlock their phones with facial recognition serves to improve a technology that has necessarily violent implications—individuals opting in, in other words, are participating in the creation and refinement of this weapon. So when we organize to abolish these technologies, we organize against their conditions of possibility as much as their immediate manifestation. We organize against the logics, relationships, and systems that scaffold their existence.


Prompt: You’re also one of the creators of the #8toAbolition campaign, which was launched by a handful of prominent police and prison abolitionists during the George Floyd protests. Among the demands listed on the campaign website are “Invest in Community Self-Governance” and “Invest in Care, Not Cops.” What might these demands look like within the context of technology?  There are groups, like May First or Color Coded LA, that are working to create movement technology, technology with a different kind of political configuration. Their experiments don’t always scale easily, because they too are working from first commitments. But investing in care and community self-governance when it comes to technology would mean supporting these kinds of experiments, helping them grow, and making them replicable all throughout the world.

Response: We need technological alternatives, particularly now. In a world where people have to talk more over video chat, for instance, it’s hard for organizations like us that are very privacy and safety focused. We don’t want people on Zoom. We need to make sure that the tools we are using are safe for our communities. So we can’t move too fast. We have to be slow, and difficult, and deliberately endure the drag because there are things that we're not going to compromise on.  Honestly, so much of our work is just mailing each other thumb drives. That’s how we do our knowledge sharing. It's not high-tech and it’s not glamorous. But that's the work that's effective in building these campaigns. It's easy to want to innovate our way to abolition. But you can't do that. You have to live in the friction. You have to be slow. You have to be methodical. You have to prioritize safety. You have to make sure folks aren’t left behind because of your sense of urgency. That’s just how it has to be done.


Prompt: During the protests, a few big tech companies also announced that they would halt or pause their work on facial recognition. IBM said that it would shelve its general purpose facial recognition product, Microsoft reported that it would stop selling facial recognition to law enforcement until there is a federal law that regulates its use, and Amazon declared it would implement a one-year moratorium on the sale of facial recognition technology to the police. How should we view such moves? I’m suspicious. These companies profit from expropriative relationships with communities that are hyper-surveilled by the state. They're not just going to give up their bread and butter.  On the other hand, in recent years, we’ve seen that there is clearly something happening within these companies. Workers are taking action. And I think these moves on facial recognition partly reflect the pressure of tech worker organizing.  To be honest, my work often has to take an adversarial posture towards the tech industry, including this kind of organizing, which is often informed by a drive to representation more than a drive to abolition. In many ways, saying that you need a more diverse, minority-sensitive tech company is like saying you need more diverse prison guards.  But as we saw with the successful campaign against the Project Maven contract at Google, this kind of organizing can make real gains. So I think their work is necessary. We need tech workers to organize so that contracts like Project Maven get cancelled. But I also think it’s necessary for organizers like myself to remain antagonistic to the very existence of companies like Google and Amazon. In my political imagination, there's room for both.  I will always say: don't join a big tech company. I will always say, you're making war machines, don't get your paycheck from them. But I also know that our work needs to be coalitional. We need solidarity between different groups that are working at different chokepoints. For instance, there are academics whose scholarship and concepts may be woefully inadequate to the work of abolition—but they’re also the ones who are teaching students before they enter the tech industry pipeline. Building a regulatory culture among technologists relies on their efforts.

Response: It’s my hope that these different communities, that are sometimes ideologically at odds with one another, can all contribute to the project of defanging, dismantling, and interrupting these systems. How do we continue to create spaces of relief and spaces of emancipation? Because at the end of the day, that may be the best we’re ever going to do.


Prompt: But isn’t the goal to abolish these systems? That's the aspiration. That's what we work towards. But what we celebrate as wins are the pauses and the breaks. We celebrate those moments where power recedes and people are able to live and to thrive. And we fight towards abolition because it’s an effective strategy to achieve those pauses and breaks.

Response: The systems we’re fighting have been around for a long time. A very long time. But if you can introduce a bit of friction, you can open up some breathing room. What is Amazon Workers International and how did it come about?  I’ll start at the very beginning just to give you the big tour.  The Poznań warehouse was Amazon’s first warehouse in Poland, and it opened in 2014. I’ve been working in that warehouse since then. When it opened, we thought that it was going to be part of a larger shift, where Amazon was going to move warehouses from Germany to Poland. There were a lot of strikes in Germany at that time, and we’ve also seen that shift before with other factories and warehouses because we are much cheaper labor than Germans. We earn about four dollars per hour, so three to four times less than they do.


Prompt: At the same time, German workers were also afraid that the warehouses were going to close and that they would lose work. We connected with some of them through social movement organizing and met in person for the first time in 2015. We quickly realized that Amazon was building power by exploiting the differences between countries. Over time, it’s become clear that the company is not closing German warehouses; they just want to use us as a cheaper, more flexible workforce to limit the bargaining power of workers in Germany. We decided back then that we needed to stay in touch with each other. The Polish-German connection was very important to the beginning of Amazon Workers International.

Response: We’ve met every year since then, twice a year, and we’ve also expanded to become a larger network that includes French and Spanish warehouse workers. Workers from a few US warehouses have also joined. The Amazon Workers International name and logo are new, but it’s not a new organization. We met in Madrid in March this year and decided to go more public.  The network relies on the organic connection between the warehouse workers from different countries—we don’t have union officials talking in the name of workers—and the fact that we share many of the same problems. Our warehouses look exactly the same inside, and Amazon uses the same disciplinary tools against all of us. We update each other about the struggles in our warehouses and think about how they’re connected. We share an understanding that because Amazon is a global company, we need to have a global movement.   To give you an example, we have very different labor laws in Poland than they do in other parts of Europe. It’s much easier to strike in France and Spain and Germany. So part of what we do together is look for ways of struggling beyond those legal differences. In Polish warehouses it is nearly impossible to organize a legal strike, but we are connected with other workers who can. That gives us power; we don’t have to sit down and cry. We can support strikes in Germany with petitions, rallies, stickers, leaflets, press conferences, actions that share their slogans—and then the German workers strike for the common cause. That’s how we fight together against something like rate increases.


Prompt: What does it look like for you all to organize in an environment where you can’t legally strike?  There was an action in 2015, a year after the warehouse opened, that gave us momentum early on in our organizing.  I was there on the night shift that night. As I mentioned earlier, we’d connected with German warehouse workers at the beginning of 2015, and we knew when they were going on strike. We’d been distributing leaflets in the warehouse and on the company buses saying the Germans would be going on strike and what their demands were.

Response: Around the same time, Amazon had announced that we’d be required to work obligatory overtime. Eleven-hour shifts instead of ten. People understood what was happening: that the Germans were going on strike and Amazon wanted us to work longer to make up for them not working. That made us really angry.


Prompt: The night of the action, everyone on the shift was talking on the company buses on the way to work. The idea was to do something in the last hour, during our obligatory overtime, but it ended up starting much earlier. The slowdown took place mostly in the Pick department. The pickers picked one item from the shelves for each tote instead of the usual twelve or fifteen. Sending the boxes to the Pack department like that made a mess of  the conveyor belts; thousands of these mostly empty Amazon totes were falling from the belt, which then brought the Pack and the Ship departments to a standstill.  It didn’t take hundreds of people. It was really clever to recognize that the Pick department is a choke point. Some people say that the Dock or Ship departments are the choke points in the warehouse since, when you do a labor action in the Ship department, you block trucks from leaving. But this was in Pick.  Pick is where they send people who join Amazon on short-term contracts from temp agencies because they can train a picker in a few hours. That’s what was unique in this action, that these workers who don’t have special training—they weren’t, you know, forklift drivers—understood how to shut down a warehouse. So it was amazing, this popular wisdom. It showed us that we don’t need a labor sociologist to tell us “do it this way” and that we don’t have to limit ourselves to the restrictive legal frames of labor and union law.

Response: What happened after that?  Retaliation. Amazon interviewed about ten workers and some of them, under pressure, signed a statement saying they took part in the action and regretted it. Amazon only stopped when we made their interrogations public. We defended a woman in court who was fired afterwards—or, she was not technically fired, but her contract was not renewed. One permanent worker was also fired and we’ve been fighting that in court for the last four years.  After the action, our union entered into a formal “labor dispute” procedure where we brought our demands to negotiations with Amazon management. It was not very useful. Our union believes that actions, not negotiations, are the best way of talking to Amazon.  In the years of organizing since then, we’ve had ups and downs. There have been other important actions, and one of them happened last summer. Five thousand workers took part in a strike vote. That’s still not enough to win the right to a legal strike because you need 50 percent of the whole company. To give you a sense, there are about 8,000 workers including temps in our warehouse, and there are nine warehouses in Poland. So while we didn’t get enough votes this time, there’s an army of people in Poland who did vote to strike. That’s what we think about work conditions in the warehouses.  Still, it’s difficult because of the permanent turnover—people joining the warehouse and then leaving, and more people on short-term contracts who don’t have labor protections, so their contracts are not prolonged if they’re going on sick leave, not meeting rate, or are open union members.  That seems like difficult terrain on which to build long-term organizing relationships. How does your union adapt to Amazon strategies like short-term contracts or their “employee forum,” which sounds kind of like an internal company union?  You have these employee forums all over Europe. Amazon uses theirs to advertise that they have “very good contact with the workforce” and “eight ways of communicating with employees.” One is the employee forum, another is a board at the company that any worker can send questions to and the board will answer. They have opinion polls every day! They’re really proud of this.


Prompt: We call it a yellow union, which is a union that was started by the boss to use against a proper worker’s union at the negotiating table. They do this so they can always bring their own union and say, “We listen to the workers, but you all are just troublemakers who promote your own interests.”  Amazon is, of course, quite clever, so they advertise this body as a form of worker representation, and there are some people who are interested in representing workers. But the critical point is that it’s not covered by labor law in any way, while the union is. If you’re not meeting rate, or if you get any form of punishment or write-up, it doesn’t matter that you’re part of that body; the employee forum doesn’t protect you. It’s fully controlled by Amazon, and they can kick you out and fire you. It’s a clever management trick.

Response: There is a general election for that body every four years. We vote on who is going to be a rep. Amazon organizes the whole process of elections, and they use the employee forum in certain formal situations. For example, by law, if there is a work accident involving a union member, the union member has the right to ask that a union representative be present. Amazon always tries to say that employee forum members should be there instead. This is a site of constant struggle for us.  It sounds like the HR department within companies, where theoretically you can report harassment to HR and they’ll help you. But that’s often not what happens because part of the HR department’s job is to protect the company from lawsuits.


Prompt: Is there a general consensus among you and your coworkers that that’s what the employee forum is for? Or are there some people who see it more positively?  Some workers find that the employee forum can be useful for solving individual problems, like if someone has an issue with their manager. But this body is legally not allowed to participate in negotiations over our essential issues: wage and rate, how much we get paid and how fast we work. They mostly just talk with Amazon about, like, where to position the fan in the canteen.

Response: Wildcat Since Way Back You talked about the importance of the fact that your union is worker-to-worker. Can you talk more about that and the organizing tradition you are coming out of? In Europe, you have many different union traditions, from business unions to more grassroots unions. And there are a lot of different union networks and organizations working on Amazon labor issues. In Amazon Workers International, we are convinced that the way we build power is in the workplace, in our local warehouses, and with warehouse workers in other countries. Our only criteria for joining is that you have to be a worker who’s organizing with others in your warehouse. That one rule reflects how we think labor movements should grow. We do not think that consumer boycotts or politicians making a spectacle of our situation will help us. That is not how we build power.  We are also different from many big unions in that we don’t have professional organizers with full-time union jobs; we all work in the warehouse. We meet directly with other workers and don’t have union bosses above us who tell us what to do. Despite language differences—because sometimes we speak seven languages in our meetings, it’s really crazy—it has been easy to find a common language with Amazon workers from different countries. If you’ve ever worked a full shift scanning items or packing boxes, you just understand how it is, how they exploit you. There is a desire to talk to each other and hear how others are fighting against things like quotas and disciplinary actions.  We are invited to demonstrations and debates with groups and networks that come from other traditions. And we do attend and cooperate. But in Amazon Workers International, we have a shared recognition of where our power comes from.


Prompt: On the organizing tradition question, I have to ask: have you read the Wikipedia article on Poznań?  No.  I was reading it ahead of our conversation and it links to an article about these protests in 1956.  Oh, yeah.  The government raised the work quota so that it wouldn’t have to pay workers at the Cegielski metal factory their full compensation. Workers responded by walking out one morning in what turned into a march of 100,000 people. Raising an arbitrary quota in order to lower pay sounds like something Amazon would do, so I was curious if that history impacts organizing in your city today.  It’s interesting that you mention this. Our union is just one section of a larger union called Workers’ Initiative. Workers’ Initiative was started in the early 2000s by workers at the Cegielski factory. What happened in that factory in 1956 was a massive moment in the history of organizing against the Communist state, and it was also connected to the Hungarian Uprising later that year. Those workers faced harsh retaliation and eighty protesters were killed. What we’ve done doesn’t compare, but we are inspired by that history.

Response: To give you a bit more of the historical context, there was a transformation in Poland when the old regime collapsed. The new regime that came into power in the 1990s was basically shock therapy for working-class people, and all the unions supported it, including Solidarność leaders who used that period to get into politics. In the 2000s, young workers at the Cegielski factory had had enough and decided that they wanted a new form of labor organizing. Workers’ Initiative came out of that. We are inspired by that tradition and the rejection of the big unions that supported company “restructuring,” which always meant dismissals.  So we are connected to that factory emotionally, but there’s another connection as well. The factory had 20,000 workers in 1956. Now it has something like 800. The old working class that made up the heavy industry sector—that factory makes engines—was destroyed in Poland in the 1990s and 2000s. Our union had a lot of discussions about what the new field of working-class formation would be. As Poland has become a big warehouse for Western Europe, we’ve come to think that logistics will be the crucial sector for the future of the labor movement.  So yes, we know the story of 1956 and we’ve tried to learn from it.


Prompt: Shifting gears, I want to ask you about the role of tech workers. Amazon recently fired two tech workers in Seattle for organizing. Shortly after that, a VP “quit in dismay” after watching the event that you spoke at, partly because he was so moved by hearing from warehouse workers. How do you see the role of tech workers in your struggle against Amazon? In Poland, we don’t really have... Well, we do have a tech arm of Amazon in Poland. We know there are a few hundred tech workers in Gdańsk working on Alexa, but we’ve never been in touch with them. Amazon doesn’t have many of these upper-level workers in our country. For the most part, Amazon in Poland is just warehousing for the West. So we don’t have an organic connection.  We do appreciate our discussions over the last few months with tech workers from Seattle. I think what they did was brave, and we need their support. We don’t have the symbolic position they have, so it’s powerful when they can give us access to the space they get. But the challenge for our work together will be whether tech workers are able to see themselves as workers who are dependent on their wages. If they are able to organize on those grounds, then we’ll have a foundation to build on together. What we would rather avoid is a situation where they only see us as pitiful, helpless people. If the only thing they do is talk about how scandalous our conditions are, that’s not useful. We need to recognize our power, and increase it together so we can make real change. The balance of power is so unequal now. We’re past the point of calling on Amazon management to make a little change here and there.

Response: Another challenge is that as warehouse workers we build our movement on our own anger; we know exactly why we’re angry with Amazon. But if you are a tech worker and you design all these tools to discipline us, your experience is very different. You have to be aware of what you’re doing. The tools they’re creating are not neutral. They’re designed to spy on us every second of our ten-hour shift, constantly increase our productivity, and literally work us to death. Last week, a worker in our warehouse died on the shop floor. The tech workers don’t see that.


Prompt: How do you explain on-call to people who don’t know what it is? I usually go broad and say something like: when something goes wrong with the product my company sells, someone has to fix it. For stuff that I work on, my team has to fix it, no matter when it breaks. There has to be someone available 24/7 to respond. The availability piece is the thing that’s hard to explain to people.  I remember there was a time when I was at the gym and my coach was asking everybody how we were doing and I was like, “I’m so tired because I was on call this week and I didn’t get any sleep last night.” And she was like, “Oh, are you a doctor?” Which is a very common response. And then I have to say no, I’m not a doctor. I’m on call for computers. And then people are usually pretty puzzled and ask if there’s really something so important that it would warrant waking people up in the middle of the night. The answer is yes, but it’s hard to explain why.  For every hour of every day, there is a person assigned to respond if something goes down.

Response: When something in a system breaks, you need a 24/7 ability to respond. There’s a lot more detail you could get into, like, how do you decide what’s worth paging about? But that’s the high-level summary. So how do you decide what’s worth paging about? I’m an engineer and I work on a team that builds products that other engineering teams at my company use. The first question that we ask is, “How do we know when our products are working?” That’s more complicated to figure out than it might seem.  We set up monitoring systems to examine different metrics. For an API, you might look at latency, which is how long it takes for a web request to be fulfilled. Or you might look at the error rate: in a perfect world, there would be zero errors when somebody tries to make a correct request to an API. But if there are errors, that could be because of a code change, or because other pieces of infrastructure aren’t working.  It’s not just about figuring out if something is broken, however. It’s also about figuring out if it’s broken enough to warrant human intervention. There’s a general philosophy that human intervention should not be the first thing that happens. If an application tries to make an API request to an external API that has nothing to do with us or our infrastructure, and that API happens to be down—it could be GitHub, npm, or any number of services—our products should be able to retry the request. If it’s the kind of thing where the request didn’t work at first because GitHub was down, but the retry worked because GitHub is back up, that is something that our system should be able to just do on its own.  But if the system can’t fix itself, then we need somebody to intervene to assess how serious the problem is, and to see if there’s anything we can do to mitigate the impact that it’s having—the fact that this thing is broken and our customers can’t use it—and then fix the thing itself.


Prompt: What are the kinds of things you personally get paged for? Broadly, they fall into two categories. One, we made a change, it didn’t go as planned, and it’s breaking things; or, two, something external to our team is broken or unexpected, so our system doesn’t work. Those are both tricky in different ways, but both of those potential failure situations inform how we build our systems and how we handle on-call.

Response: What do you mean that it would affect how you build systems? We’re on call for systems we’ve built, which is a very particular on-call philosophy. There are some places where these people over here create the thing, and those people over there are on call for when the thing breaks, and those are totally different teams. But we’re on call for systems that we’ve built ourselves, so we have to expect that components of our systems will fail, and we have to integrate that anticipation of failure into what we promise our customers. And we have to think about how we architect and monitor for failure.  Do you have a sense of how many people are impacted by an outage that you’d get paged for? Is there a way to measure impact? Absolutely. One of the first things we measure is customer impact, and that determines the severity of the incident we’re dealing with. On one end of the spectrum, the least impactful end, our team will have a conversation about whether we should even be getting paged for something like this. Maybe the answer is no and we change how we’re alerted, or we make the system more robust so it doesn’t experience that failure anymore.  On the other end, I’ve responded to pages for downtime, which means that external customers cannot use our product. That’s typically measured as a percentage. So we’ll say, “This outage impacted 5 percent of our customers globally” or, “10 percent of our customers in this particular region couldn’t use our product for fifteen minutes.”  The scariest failure I can think of that my team would be on call for is if our content delivery network (CDN) went down. That is the point of entry for customers who use our service and it handles billions of requests every day. So even if everything behind the CDN is working correctly, if there’s an outage at the point of entry, that would impact a lot of people. Like, potentially all of our customers.


Prompt: I have this image of one person being woken up in the middle of the night because a million people can’t access the app. It sounds like that’s not how this works, though. If a million of our users are affected and only one person is waking up to deal with it, that’s wrong. A company with a million users has hopefully put enough thought into how they do on-call that an outage of that size wouldn’t happen that way. All of that said, if something like that did happen, the one person that gets paged would then page a bunch of other people once they realized that something was very broken. When there’s an incident of that scale, whole teams are brought in to help and someone is the “incident commander” who coordinates the response.

Response: Still, this doesn’t happen that often. A lot of people think of outages as all or nothing. But it’s not usually the case that a huge number of our customers can’t use any of our services all at once. The more likely scenario is that one of our services goes down and it’s part of another company’s checkout system, so their customers can’t pay. Or maybe their app doesn’t load properly on their customers’ phones if one of our services is broken because of how the two are tied together with code. That company’s customer has no idea that the problem lies with us. But they get impacted by our outage nonetheless.  Even with those smaller incidents, however, a lot of money can presumably be at stake.


Prompt: Yes, and if it’s our fault, the companies that rely on our services can come to us and say, “We signed a legal contract where you promised 99.999 percent availability,” or whatever percentage we promised them. There’s this concept of the number of nines of availability a service has. This indicates how available you expect a service to be, because it can never be 100 percent. For instance, a service that is 99.99 percent available has four nines, while a service that is 99.9999999 percent has nine nines. You build your expectations around how close or far from 100 percent availability a service is. The more available a service is, the more other companies rely on it in building their own products.  So if we breach our obligation around availability, a company might ask us for a refund or make a decision to not use our product anymore. We might do the same thing if another product causes us downtime. When there’s an outage of something we rely on, we’ll go to the company and say, “We want a root cause analysis, we want to know what the fix was, and we want a refund.” They can’t just respond and say, “I dunno, something broke but it’s good now!”  It gets trickier when you’re locked into a specific vendor. In some cases, we’ve decided to be locked in, in part, because they promise a lot of nines and we pay a ton of money, so that when their failure affects our failure, we get details as soon as possible, we get information under NDA to understand what happened, and we can ask for more help in how our relationship works.

Response: Waking Up is Hard To Do If you’re on call outside of a workday, what does that mean for your personal life? How does it affect your evenings and weekends? Well, that’s definitely when I notice on-call the most. I may try to go to sleep earlier on nights when I’m on call because I can’t guarantee that I’m going to sleep through the night. I can’t make spontaneous plans when I’m on call unless I carry my laptop around with me. So those weeks require a lot more planning.


Prompt: Because being on call means you literally have to open your laptop and debug as soon as you get paged. We’ve been talking conceptually about what it is and the philosophy behind it, but that’s what it literally looks like. Right. Let’s say I get paged when I don’t have my laptop or I’m out without my charger. I would escalate immediately to make sure that somebody else responds. But typically, we respond within a few minutes. We respond as if everything is urgent. If I get woken up in the middle of the night, my sleepy brain is like, “You can look at it later,” but I’ve trained myself to not look later, to look now because it could be really bad. But yeah, it can really impact my life, my ability to do errands, my sleep. Bad weeks are bad.

Response: What do you do when you get paged? Almost everyone I know who’s on call, regardless of the company, uses the same app to configure pager alerts. And you can configure the app to send different kinds of information each time it pages someone. We have a link to whatever metric is passing the threshold that’s causing the page. Those metrics are also things that we configure.


Prompt: Throughout your code?  Right. All the services that make up what looks to customers like one cohesive product are owned by different teams and the teams set up the thresholds they want, and people get paged based on those thresholds. A threshold might be: “If this function fails five times in an hour, page someone.” There must be dozens of those? Thousands? A lot. That’s why it’s important to be thoughtful about what you want to be alerted to and what the threshold for an alert is.  So coming back to your question about what I do: I first look at the metric that has passed the threshold. Then, I look for documentation about that alarm. When we create an alarm, we try to write documentation on what it is and what it’s measuring. If the docs are good, they also include context about why this piece of code or infrastructure exists and its various potential failure states.  But you don’t want to get into too much detail on each failure state. Sometimes, when people deal with failure, their instinct is to say, “If I document every single piece of information about this situation, I will know exactly how to respond when this happens again.” But if you have something that fails regularly, for a very predictable reason, you should fix the problem in the product and stop paging everyone all the time. Although sometimes that’s easier said than done. It is pretty easy to document all the ways in which something can break—it is usually much harder to build something that breaks less often.

Response: Sometimes, I get paged for something I’ve never worked on before. That’s when I really lean on this process. I see a metric. I see some docs. Something is broken that’s potentially impacting people. How do I use these pieces to get to an understanding of what happened and how to fix it?  Does everybody really wake up and deal with a page they get at two in the morning? Surely, people sleep through alarms. What happens then? Yes, everybody really wakes up. There may be some rare case where, you know, someone got a new phone and didn’t set up their notifications correctly. Or, people accidentally sleep through middle-of-the-night alarms. I certainly have. But if someone were to repeatedly refuse to respond at night, they just wouldn’t last in that job. What happens when someone misses a page is that the next person gets paged. The app is configured to page a certain number of times in a certain number of ways—it’ll text you, then call you, then email you—but if you don’t acknowledge the page, it tries the next person.


Prompt: In the same way that computers are automated to fail over to the next system, the app will fail over to the next human if one of them is down. Yeah. If I sleep through an alarm, our escalation is set up to try my team members first. Then my boss, then my boss’s boss, all the way up to the executives. If all of us sleep through all the alarms, the CEO would get paged. I’ve never seen that happen before, though.

Response: What has happened is that I’ve been paged for something, didn’t know how to deal with it, and then paged someone else to wake them up to help. How does it feel to do that? I mean, I wish I never had to do that. It sucks, because I know how garbage I feel after I’ve been woken up at that time. But this is actually a place where team culture is important. If someone else wakes me up, I try to respond without resentment and without making somebody feel bad for needing help. We don’t page each other frivolously, but if someone doesn’t know what to do and I’m second in line, it is my job to respond and help that person out. It can create a really toxic culture if you’re like, “Ugh, why did you wake me up for this?” And if somebody stops asking for help, that is a big potential failure scenario. That’s why, when we onboard someone, we really play up the “It’s totally super fine! Don’t worry about it, page me anytime!” They won’t actually page me anytime, but it’s important for them to know that they can if they’re in trouble.


Prompt: The company probably benefits from people being kind and showing up for each other in cases like that. Definitely. I mean, on-call can go lots of ways. What I’m describing, even if I don’t love on-call, is being on a team with people I trust, knowing that I won’t get yelled at or fired for unintentionally doing something that causes damage, and knowing that there’s a genuine spirit of reflection around how to fail better. The thing that motivates me during on-call, much more than fixing the tech, is my teammates. There are things that are beyond our control: there’s a lot of failure on the internet and we don’t pick the days when a critical service goes down. If everyone is always exhausted and grumpy when they show up, that sucks for them and it sucks for me. So, almost always, if someone gets woken up in the middle of the night, another person on the team will offer to take over their shift the next night so they can get a full night’s sleep. Because waking up one night sucks, but waking up two nights in a row? You’re toast.  We also encourage one another to ask for help and to offer help. If we’ve identified something that is really disruptive to each other’s day-to-day lives, we take that seriously and make changes so that that thing doesn’t happen anymore. That matters when you think about the fact that we are on call for holidays and weekends. There’s a lot of motivation for us to make on-call not terrible. So we are caring for infrastructure, but ultimately we’re taking care of each other.

Response: “Taiwan is a paradise bubble,” my dad told me in March, during my first few days back at my parents’ home. “This is probably one of the safest places in the world right now,” he said. Seeing the rush hour crush on the Taipei metro and children in school uniforms clustering at bus stops after school, all without exhibiting signs of fear or anxiety, I couldn’t agree more.


Prompt: When the pandemic began, I had been in New York. Throughout February and early March, I checked in frequently with my parents in Taiwan. Things are fine here, they said. Meanwhile, the situation in New York was worsening. Cases were beginning to appear, but the government response was hesitant and nebulous. The virus is coming, warned the media. It may already be here.  If it’s already here, I wondered, why aren’t we doing anything about it? Why is everything continuing as normal? The situation felt out of control from the start. My parents urged me to come to Taiwan. On March 14th, 2020, I flew to Taipei on a direct flight.  My parents were right. From what I can tell, apart from masks on every face, life in Taiwan is uninterrupted by the pandemic. Schools, pharmacies, post offices, convenience stores, and parks are all open. Coffee shops, in abundance in Taipei, are full of people. Even the shopping malls are operating at regular capacity. Aside from a handful of attendants stationed at the front entrances, armed with temperature readers and hand sanitizer spray, every store remains open.

Response: Early on, experts predicted that Taiwan, due to its close proximity to mainland China, would have the second-highest number of COVID-19 cases outside of the mainland. But the predicted wave of infections never materialized. As of late June 2020, Taiwan had reported only 447 known cases and 7 deaths. Compared with worldwide figures, these numbers are shockingly low; by comparison, more than 22,000 have died in New York City alone.  As countries around the world struggle to contain the virus, many observers are looking with great interest at Taiwan’s success. Rarely making international news and typically only in connection with mainland China, Taiwan has lately been held up by journalists and academics as a model for how to manage the pandemic. How did Taiwan, with a population of twenty-three million, eighty-one miles away from mainland China, with over 800,000 citizens working there and frequently traveling back and forth, manage to avoid the public health crisis that is now destabilizing the rest of the world? One part of the answer, I discovered, is a unique mix of technological interventions—some led by the government, others coming from the grassroots—that have helped coordinate the massive mobilization of people and resources required to fight the virus.  Fenced In When I arrived in Taipei, I sailed through the airport. “Where are you coming from?” a health official asked. “New York,” I said. She took a health self-assessment form that I had completed, handed me a slip of paper with instructions for how to monitor my wellbeing, and waved me on. I had expected a more rigorous interrogation.  It turns out I had returned to Taiwan just in time. Had I landed two or three days later, when the authorities raised the United States’s travel advisory from Level 1 to Level 3, my experience would have been radically different. My friend Ting wasn’t so lucky. She arrived later than I did, also traveling from New York. And it was through her experiences that I first began to learn about the role of technology in Taiwan’s pandemic response.


Prompt: By the time Ting left New York, the city had become a viral hot zone. Before she departed, Taiwanese authorities required that she fill out an online health screening form, providing her medical information and, most importantly, an address where she could quarantine in Taiwan. Upon landing, all passengers underwent testing, the results of which were given two days later. There was also a changing area where passengers could change out of their plane clothes. Alcohol was provided for full-body sterilization. Ting passed through immigration and was escorted to a special quarantine taxi, to take her and other travelers to their quarantine locations. She was required to spray herself down with alcohol again before entering the cab.  When she arrived at her apartment, she was called by a local healthcare official (衛生所) assigned to her. They exchanged contacts on LINE, a popular messaging app in Taiwan similar to WhatsApp, to stay in touch. She also had to join a special LINE group, where she was expected to report her temperature and wellbeing twice daily, at 9 a.m. and again at 3 p.m. Soon after, on that same day, a government worker arrived at her door with a bag of supplies. This included garbage bags, extra masks, and some food. She signed some paperwork that stated she would commit to the entire fourteen-day quarantine process without leaving her apartment. If she had not owned a cell phone, the government would have provided one for her.  How would the government know whether Ting kept her quarantine? The authorities use mobile phone location data and cell tower triangulation to draw a “digital fence” boundary around an individual’s home. If you step outside of this zone, or if you turn off your phone, an alert is sent to the police and local health officials. Ting tells me a story of a friend whose phone shut down suddenly while in quarantine. Within a minute, the police knocked on his door.  When I asked Audrey Tang, a former computer programmer and tech entrepreneur who now serves as Taiwan’s first Digital Minister, about the privacy concerns raised by the digital fence, she was quick to point out the ways in which the program differed from surveillance tools used in other countries, such as smartphone apps or physical bracelets. Tang believes the Taiwanese approach is far less harmful. “First, it’s not GPS,” she says. “We are not asking you to install an app that reports GPS.” The level of location specificity provided by such information would be unnecessary for enforcing home quarantines, she explained.

Response: Moreover, telecommunication companies are already collecting the data being used to construct the digital fence. “This is not new information being collected,” Tang emphasized. The emergency warning broadcasting system, which sends texts about flash floods or earthquakes, relies on the same data. “Instead of collecting new data or requiring you to install a new app,” Tang said, “we repurpose existing data and existing notification mechanisms.” Most importantly, the program is sharply circumscribed: the data is only used to implement the home quarantines, Tang insisted, and nothing else.  Fork the Government Technological solutions have not only come from the top down, however. They have also come from the bottom up. This is due in large part to Taiwan’s uniquely robust civic technology community, known locally as g0v (pronounced “gov zero”). In its current form, g0v grew out of the Sunflower Movement in 2014, a student-led protest sparked by concerns that a trade pact with mainland China would give Beijing more influence over Taiwan. But protesters also demanded more transparency and accountability from the Taiwanese government—demands that were taken up by the g0v movement.


Prompt: Using the language of open-source programming communities, g0v claims that its goal is to “fork the government.” Digital Minister Audrey Tang, who is herself a veteran of the Sunflower Movement and a longtime contributor to g0v, explained the concept to me. Essentially, it means that g0v hackers produce alternative versions of government websites. “For each government website—which always ends in ‘gov.tw’—that they don’t like, they just change the ‘O’ to a ‘0’” in the domain name and create their own, Tang said. In the process, the g0v community has implemented a wide range of digital tools designed to increase popular participation in policymaking, from online platforms for circulating petitions to data visualization dashboards that help citizens understand how budgets are allocated.

Response: When the pandemic began, g0v responded creatively, using its “fork the government” model to help the authorities contain the virus. Perhaps the best known example is the collection of digital tools—apps, maps, chatbots—that g0v hackers created to make it easier for the public to buy masks through the government’s mask-distribution system.


Prompt: Due to its previous experience with SARS, also a coronavirus, the Taiwanese government recognized early on the importance of mask-wearing to reduce the spread of COVID-19. The speed with which the government reacted most likely prevented a more serious outbreak in Taiwan. Taiwan reported its first case on January 21st: a woman traveling back to Taiwan from Wuhan. The next day, the government banned all Wuhan residents from entering Taiwan, the day before Wuhan went into lockdown.  Although the government made assurances to the public later that week about a sufficient supply of surgical masks, fear of future shortages led the government to halt exports of masks on January 24th. To control the mask supply, the government also decided to implement a mask distribution system, while working with local mask manufacturers to ramp up production.  Masks were initially sold in convenience stores. With over 10,000 convenience stores on the island, Taiwan has one of the world’s highest ratio of convenience stores to population. Convenience stores in Taiwan provide services beyond what American convenience stores offer. You can pay your phone, electric, and utility bills; your taxes; and even your parking tickets there. You can buy everything from disposable underwear to towels to books to train and concert tickets. It even functions as a post office. Similar to Amazon Lockers, online packages can be delivered to any 7-Eleven or FamilyMart for pickup.

Response: Convenience stores, then, were an obvious choice to serve as mask distribution hubs. However, it soon became apparent that the stores did not have a means of verifying people’s identity via their National Health Insurance (NHI) card to approve mask purchases, without violating personal privacy by collecting this data themselves. Eventually, a card scanning system was introduced to bypass data collection. But these challenges pushed the government to switch to selling masks from pharmacies, which are already connected to the NHI database. This allowed people to maintain their privacy and gave the government the ability to monitor the rationing of masks.


Prompt: Mask rationing had been put into place on February 6th, within a week of the government launching the mask distribution system. To prevent hoarding, every Taiwanese adult with a NHI card was limited to purchasing two masks per week. To reduce congestion and long lines, the ability to purchase masks was also systematized. The last digit of your NHI card ID number indicated on which days you could purchase your masks. ID numbers ending with an odd number could purchase them on Mondays, Wednesdays, and Fridays; even numbers on Tuesdays and Saturdays. Sundays were open to everyone.  The government also worked to keep prices low, threatening to fine sellers that were inflating prices up to $50 million TWD (a little over $1.5 million USD). These moves were designed to ensure that every citizen would have the opportunity to purchase a sufficient number of masks for daily use at an affordable rate.

Response: Despite these measures, however, the early days of the mask distribution system were plagued with problems. In February, when the system was still in its infancy, masks were selling out fast and the public was having a hard time finding them. There were frequent stories of people waiting in long lines to purchase masks, only to have the pharmacy run out when they got to the front. There was no immediate way of verifying whether the pharmacy you went to had enough masks until you physically arrived on location.


Prompt: In response, g0v hackers came up with a solution. The idea first originated in the g0v Slack channel: a digital map that would visualize the quantities of masks available in different pharmacies. Howard Wu, a programmer and member of g0v, noticed that many of his family and friends were sharing information in LINE groups about which convenience stores still had masks in stock, back when convenience stores were the primary places to buy masks. He built a real-time “Mask Map” which relied on crowdsourced data to display mask stock levels in different stores. Users’ geolocation data would help them find nearby stores. Since there weren’t any existing comprehensive GIS datasets of convenience stores in Taiwan, Wu used Google Maps to obtain this data. Wu’s site had roughly 550,000 visits within the first six hours.  But relying on crowdsourced data wasn’t accurate enough. Digital Minister Audrey Tang showed Wu’s work to Taiwan’s Prime Minister, who immediately understood its usefulness. The government recognized that it could improve the accuracy of such civic digital tools by providing more up-to-date data. On February 4th, two days after Wu released his digital map, the government announced the switch to selling masks from pharmacies. In a coordinated effort with Tang, the Ministry of Health and Welfare released mask inventory data at pharmacies nationwide that was updated every thirty seconds.  Wu created another version of his site with the new data—and received 830,000 hits on the first day. Soon after, using the API that Wu had built for his map, g0v hackers created dozens more digital tools to help track mask availability, from more maps to smartphone apps to LINE chatbots. A government website now lists over 130 digital products for tracking mask inventories in Taiwan, all built by civic technologists.

Response: The maps and apps have not only served as useful tools for people trying to purchase masks, however. The government has also relied on these tools to improve its own distribution supply chain. Officials have been able to track the fluctuating numbers in different cities and provinces, which they can use to adjust mask shipments in real time. This reciprocity between the government and the grassroots technologist community has greatly benefited both parties, and Taiwan as a whole. It also stands in sharp contrast to the top-down approach of mainland China, where technological interventions to contain the virus have taken a far more authoritarian form.  Care Works There is no doubt that initiatives like the digital fence program and the g0v mask maps have contributed to Taiwan’s effective management of the pandemic. As mechanisms to help coordinate the allocation of people and resources, these digital tools have proven invaluable. But the more I read, observed, and talked to the people around me, the more persuaded I became that technology had more of a supporting role in Taiwan’s success.


Prompt: When I ask my friends and family what Taiwan did right, they rarely mention technology. Instead, they talk about the soothing ritual of tuning in every afternoon to the Taiwanese CDC’s daily press conferences, which are led by doctors, epidemiologists, academics, and public health experts rather than politicians. They talk about the government’s decision to give journalists unlimited time to ask questions, and how this has resulted in more accurate media coverage, less disinformation, and greater public trust in the information being conveyed by the authorities. They also talk about the CDC’s toll-free hotline, where you can call and talk with a person about anything related to the virus. One friend of mine, worried she had contracted COVID-19, called the hotline multiple times. She described to me how comforted she felt being able to talk to someone who could ease her fears.

Response: In part, the Taiwanese government’s multi-faceted communications strategy reflects an attempt to make up for past mistakes. The government’s mishandling of the SARS epidemic in 2003, which had a lower case count but a higher death rate than COVID-19, severely undermined public trust at the time. Unaware of the highly infectious nature of SARS, one woman’s visit to an emergency room set off a chain of transmission that spiraled out of control. In a desperate attempt to contain the virus, the government sealed off Hoping Hospital, with more than 1,000 people, infected and uninfected, locked inside. The inhumaneness of the approach shocked Taiwanese citizens. Twu Shiing-jer, Taiwan’s Minister of the Department of Health, resigned in the aftermath.  Post-SARS, Taiwan immediately began planning for the next health crisis. It could not afford to be caught off-guard again—especially since it had been clear during the SARS epidemic that Taiwan would have little to no direct communication with the World Health Organization, because it is not a member. Taiwan is isolated, and on its own. This realization may have proven decisive in its pandemic response, as Taiwan was one of the earliest countries to sound the alarm on COVID-19 and begin monitoring the virus.


Prompt: Above all, when I talk to Taiwanese people about what Taiwan did right, they talk about healthcare. In particular, they praise Taiwan’s single-payer healthcare system. Almost 99 percent of Taiwanese citizens and residents are covered by Taiwan’s national health insurance program. (The 1 percent, the government believes, consists of Taiwanese citizens residing outside the country.) When coverage hovered around 96 percent, the government made a concerted effort to track down the remaining 4 percent—composed primarily of Indigenous Taiwanese, the unemployed, the homeless, and orphaned children—to get them enrolled. Households below the poverty line receive free coverage. Essentially, no one is denied healthcare in Taiwan.

Response: With 99 percent of the population insured under one system, a centralized medical database made it possible for the government to rapidly implement its mask-rationing system. It also made performing contact tracing easier, as well as tracking community-based transmission. More importantly, universal health coverage means people aren’t afraid of being denied medical treatment or going bankrupt from medical bills. The government encourages citizens to report even mild symptoms, which enables the authorities to detect infection earlier.  Ultimately, Taiwan’s success in containing COVID-19 has less to do with technology than with well-functioning state institutions that acted quickly and collectively. As a Taiwanese friend described it, the government’s approach has resembled crossing a river. You inch forward step by step, feeling your way across and making decisions as you go. Along with this experimental, adaptable spirit, the government’s focus on transparency and building public trust, paired with an excellent universal healthcare system, are the real strengths of the Taiwan model. Technology, while useful, cannot make up for the absence of strong public structures of care. In the United States, where the fight for universal healthcare is still an uphill battle, care is a luxury good with multiple prerequisites—employment, wealth, geography. In Taiwan, care is a basic human right that everyone receives equally.


Prompt: In early 2017, an investigative journalist uncovered a private Facebook group called Marines United, where hundreds of veterans and active-duty marines were circulating nude or invasive photos of military servicewomen without their knowledge. “Dozens of now-deleted Google Drive folders linked from the Facebook page included dossiers of women containing their names, military branches, nude photographs, screenshots of their social media accounts and images of sexual acts,” the journalist, Thomas Brennan, later wrote for the news site Reveal.  The Marines United scandal stood out for the coordinated nature and scale of the abuse, but it was only one egregious example of how toxic social media platforms have become. According to a study by the Pew Research Center in 2017, 41 percent of Americans have personally been subject to abusive behavior online, and one in five have been the targets of particularly severe forms such as sexual harassment, stalking, revenge porn, physical threats, and sustained harassment over time. Those who experience online harassment suffer from mental or emotional stress or even fear for their personal safety, and the stakes are particularly high for young internet users: other studies have found a significant association between cyberbullying and depression, self-harm, and suicidal ideation in people aged 12–18.  Following the Marines United revelations, Facebook quickly shut down the private group, but similar ones immediately began cropping up on the platform. Most major social media platforms ban sexually explicit photos, particularly those flagged as non-consensual, so if a victim sees the content they can report it. But this doesn’t keep the images from being shared on private groups where they are less likely to be reported. As a result of mounting pressure from advocacy groups for victims of sexual assault, Facebook promised to take a more active role in addressing such harassment.  The result, which Facebook began rolling out in April 2017, and which now includes partnerships with nine organizations across eight countries, is one of the most proactive efforts by any of the social media companies to address online abuse. And yet, despite the amount of time and money that Facebook has spent on the program, the whole thing is ultimately doomed to fail. It’s a revealing failure, though, because it points to fundamental limitations in the way that social media companies think about—and have encouraged the rest of us to think about—the problem of online harm.

Response: The Narrowsight Board Facebook’s process for addressing revenge porn and other non-consensual sexual images requires victims to upload their images to the company, where a “specially trained” employee reviews the image and then creates a digital fingerprint of it. This allows image-matching software to detect if the same photo appears elsewhere on the site or is later uploaded again. This can potentially help some victims, but it has major shortcomings. It assumes that the victim has access to the non-consensual image, and it requires that the victim trust Facebook with extremely sensitive content. The image-matching software is also remarkably easy to fool; slight alterations to an image, such as changing the background, have been shown to elude the technology.  More importantly, the technology takes control away from the victim by assuming that deleting the images automatically is all that a victim wants. The victim never learns whether anyone else has tried to upload the images and has no proof for further action. Platforms even routinely ignore “preservation letters” from lawyers of victims of revenge porn, and delete crucial evidence.  At the most basic level, Facebook’s process, like other attempts to address online harm, suffers not from faulty algorithms, but from a crucial misrepresentation of the problem. Social media companies have construed a wide range of online harms as essentially problems of content (violating photos, violent or threatening posts, Nazi symbolism). As a result of this framing, the solution to online harm has largely been presented by these companies as “content moderation”: removing posts that a platform deems against the rules or toxic, and occasionally banning the user who posted the content.  Social media companies have a strong incentive to adopt the content moderation framework, which was originally developed to minimize spam, and all of the large social media companies moderate content to some extent. That’s because the quality of their platforms would spiral downward if they didn’t. Imagine logging into Facebook and seeing an unabated stream of violent images and junk messages—you probably wouldn’t want to log in again. Most advertisers don’t want their ads to show up next to such content either. A decline in user engagement and ad sales is bad for a social media company’s bottom line, and removing potentially offensive content is the cheapest way to ensure that doesn’t happen.


Prompt: But although it might maximize profits, this way of thinking about online harassment is almost entirely unable to address the harm that harassment causes. It assumes that the problem is individual pieces of harmful content that must be moderated—not people and their relationships. As a result, content moderation fails to serve the needs of those who are harmed online or to change the conditions that make such harm possible.

Response: Once the problem of online harm is framed as content moderation, it is already a lost cause for victims. Inevitably, platforms claim that the sheer amount of content makes it impossible to monitor. This is true, but it conveniently leaves out the fact that every single decision made by platforms prioritizes scale, and platforms generally avoid taking actions that might reduce user engagement. At the same time, as the scholar Sarah T. Roberts has detailed, they strive to minimize costs, especially for things like human moderators.


Prompt: If platforms accept that something needs to be done about online harm, but frame the problem as needing simply to detect and remove content that breaks platform rules, the logical next step becomes automating this process. When Mark Zuckerberg was questioned by Congress in 2018, he mentioned “artificial intelligence” more than thirty times, framing it as “the scalable way to identify and root out most of this harmful content.”  Many experts disagree that AI will ever be able to apply content moderation rules effectively by itself. Even if it could, abusers quickly adapt to automatically enforced content moderation rules, for instance by editing non-consensual sexual images to bypass AI detection, creating fake porn videos of victims using deepfake techniques, or by coordinating attacks and using memes to mask their intent. What the vague promise of a forthcoming technological solution has been effective at doing is dampening calls for greater regulation of social media platforms.

Response: The content moderation framework also severely limits our collective ability to push back against the way social media companies deal with harm on their platforms. Most platforms have no mechanism for contesting moderation decisions. In response to pressure from users and lawmakers, Facebook recently created an oversight board for content moderation, but because the problem itself is so narrowly defined, so is the solution. The oversight board can only review individual pieces of content, and only if the author of the content objects to their removal from the platform. This means that the oversight board has no influence over what content is allowed, how private groups are governed, or what the platform’s algorithms promote. Nor can it do anything to support people who have already been victims of harassment.


Prompt: In order to do that, we need to think differently about the problem of online harm. Over the past two years I have been working with collaborators in communications, information studies, and computer science to imagine alternatives to the content moderation model. Our greatest resource has been abolitionist traditions that have challenged the criminal justice system for dealing with offline harm, and which have been extremely influential in shifting public perceptions of policing in the wake of the police murder of George Floyd.  In particular, those rallying to abolish prisons have developed models of restorative and transformative justice that can expand our imaginations of what can be done to address harm more generally. In listening to members of online communities, my research group has found that what is needed is not more sophisticated ways to identify and remove offending content—just as we don’t need better ways of policing and imprisoning people—but ways of supporting survivors and transforming the societies in which harm happens, including our online social worlds.

Response: Obligation and Restoration Practices of restorative justice, which have their roots in Indigenous ways of repairing harm, are particularly useful to rethinking how we address online harm.  Restorative justice views harm not as a crime against the rules of the state, but as a violation of people and their interpersonal relationships. Violations create obligations, and the central obligation of restorative justice is to right the wrong. At minimum, restorative justice requires that we address the victim’s needs related to the harm; hold offenders accountable to right those wrongs; and involve victims, offenders, and communities in this process.  The primary tool of restorative justice is communication, and practitioners have developed structured modes of communication to take people through a process of reckoning with and repairing harm as much as possible. Although restorative justice has to be adapted to different communities and circumstances, its principles have been successfully codified into processes and formal training and embedded in churches, workplaces, and neighborhoods. In Oakland schools that have implemented restorative justice, students feel more respected by and connected to their peers, and detention rates have fallen to zero.


Prompt: What would an approach to online harm grounded in restorative justice look like? My research group has conducted extensive interviews with restorative justice practitioners, as well as with the moderators of online communities on platforms such as Reddit and Discord. As part of this work, we practiced speculative design: giving participants real-world scenarios of online harm and asking them to discuss the ways that those harms might be addressed, including assessing which scenarios that were most likely to happen, most desirable, and most unexpected. Here is a basic table contrasting the kinds of questions that would be asked when using a content moderation framework versus one based on restorative justice: Content moderation What content has been reported? Is the content against the rules? Should the content be removed, demoted, flagged, or ignored? Restorative justice Who has been hurt? What are their needs? Whose obligation is it to meet those needs?  For example, in the case of non-consensual sexual images like the ones shared in the Marines United scandal, a restorative justice process might begin with the following questions:  Who has been hurt? The person whose private, intimate photos were shared without their consent. What are their needs? Victims have a diverse range of needs, including: protection, if they are in immediate danger; validation; and contextual information (Are they being stalked? Have the images been shared? Where?). While “blocking” is often presented to victims as a way to deal with abusers, in practice many victims seek to actively monitor their abuser so that they are not caught off-guard. Some victims want the harm to be acknowledged by the person who committed it, or by the community. Many victims want the harm not to happen again, to themselves or anyone else. Whose obligation is it to meet those needs? The obligations mentioned above fall to many people: the person who committed the harm, the wider community of people who know and care about these people, and the platform that provided the space for the harm to occur.

Response: While the obligations of the person who committed the harm and of the wider community have been extensively discussed by restorative justice researchers and practitioners, the obligations of platforms are new and unique to online harm, and deserve special attention. Each case is different, and should be addressed individually and with care, but here are several possible approaches, which may need to be combined: Assign a trained caseworker. In the most extreme cases of harm, such as sharing non-consensual sexual images, trained caseworkers should be assigned to the case. Facebook currently assigns a “partner” to someone who seeks to report revenge porn, but their role is only to instruct the person on how to upload their images to Facebook. Instead, the case worker should support the victim, provide validation, share information on their options, and help them identify and activate a community of people that cares for them and can be involved in the restorative justice process.Be trauma-aware. Current methods of addressing online harm, including Facebook’s revenge porn process, are detached from the reality of being a victim of sexual harm. For instance, sharing private, intimate photos with unknown Facebook employees can retraumatize the victim. An alternative could be giving victims the tools to create a digital fingerprint of the image themselves with the help of trained outside support groups, who could verify and share the symbolic identifier with Facebook without sharing the actual photos.Support the harmer to take accountability. With the victim’s consent, their case worker may reach out to the offender, have a conversation about the harm, and gather and provide information. At present, Facebook deletes the photo or prevents it from getting uploaded, and in some cases they ban the offender with little explanation. This actively discourages responsibility on the part of the offender. But offenders need to be encouraged to understand the harm and to work towards repairing it. That might mean apologizing and working to not repeat the harm, becoming educated about the broader effects of the harm, or paying reparations for harms done in the past.Stop the continuation of the harm. This may mean placing limitations on posting, or even removing the person who has committed harm from the platform. This does not mean that anyone who commits harm should be immediately banned forever, but does mean that removal from the space is a valid and possible consequence of harm. Removal should be done with care so that it is not simply a punitive response but is actually necessary to stop the harm and is done with explanations and an opportunity to engage with the process.Create structures for continued accountability. Platforms should make addressing harm a priority before it occurs. This can mean setting expectations that sexual harm will not be tolerated when members join, and periodically reminding them as well as explaining what accountability protocols exist to address harm. It can also mean training and supporting users to intervene in sexual violence. Facebook Groups currently rely on admins from the group to moderate it; the platform could mandate that groups above a certain size conduct a yearly review of their protocols for dealing with sexual harm, and this could include designating at least two point people to whom such issues should be reported and a process for addressing incidents.


Prompt: Communities of Care Restorative justice approaches to online harm require resources, time, training, and labor. Each harm case—from Donald Trump threatening violence against protesters to trolls sending journalists violent memes—will be unique and require a creative response. It’s a revolutionary idea that victims of harm, who are often people with the least power in society, should be cared for, and we cannot expect today’s large social media companies to commit the resources necessary to do this of their own accord. But we can pressure them to, and we can create alternative platforms that do.

Response: The project of content moderation is costly, complicated, and controversial. To move beyond it, platforms need to spend considerably more resources to hire experts who are trained in restorative justice, trauma response, community accountability, and anti-racism. They would also need to help fund outside organizations such as victim support groups. This would mean lower profit margins; it would also mean adapting protocols to different cultures and geographical regions.  All of this would be difficult and expensive to achieve at the scale of current platforms, which is an argument for breaking those platforms down into smaller online communities governed by the users themselves. If companies whose main activity is to host user-generated content can’t effectively address harm on their platforms, maybe they shouldn’t have such high profit margins—or maybe they shouldn’t exist at all.


Prompt: My research group has begun to practice some aspects of restorative justice in online communities in coordination with the moderators of those communities. Pre-conferencing, which involves one-on-one conversation between the mediator and different people involved in the harm, is often the first step of a restorative process. In order to get a deeper understanding of the types of harm that happen, the needs of those who are harmed, and what potential next steps could look like, we are currently conducting pre-conferencing interviews with people who have been harmed in online gaming communities, those who have been banned from certain games, and moderators.

Response: In building a just future we cannot however rely solely on the intervention of platforms, or on restoring justice one harm at a time. Even as we work towards restoring justice right now, our long-term aim must be to transform the societies in which harm occurs. This is the work of transformative justice, which was popularized by women and trans people of color as a way to address interpersonal violence and tie it to structural and systemic forms of violence. As the organizer and educator Mariame Kaba puts it: “I am actively working towards abolition, which means that I am trying to create the necessary conditions to ensure the possibility of a world without prisons.” The future we should be working toward is one in which every single person has the skills to identify harm, hold themselves and others accountable, and work towards justice. At the same time, we must transform the social conditions, including patriarchy and racism, in which harm thrives. This kind of work leads us to fundamentally transform our relationships with one another, and it cannot be scaled or outsourced. When building a future that addresses online harm we should not seek mere alternatives to content moderation; we should work towards a world where no content moderation is needed.


Prompt: One morning this June, I sat in my car at the drive-through of a local pharmacy, waiting for a COVID-19 test. Speaking through an intercom, a woman wearing a mask walked me through the process of administering the test on myself: where I could find the cotton swabs, how to open the vial of preserving saline, how to properly swab my nose, including the fifteen-second countdown for each nostril. She explained where there was an additional wipe for my protection when opening the biohazard bin in which I placed my sample, as well as how to wipe to protect others.

Response: The woman administering all of this care was among the legions of specially trained frontline health workers needed to help manage the coronavirus crisis in the United States, under a dire lack of federal coordination and guidance. In the early stages of the pandemic, nurses around the country struggled to train enough people to provide care, especially after officials closed schools and other usual educational avenues. Technology, however, seemed to provide a ready solution: the National Council of State Boards of Nursing and the American Association of Colleges of Nursing, among others, recommended using computer simulations and online learning platforms to upskill the nation’s healthcare workers.


Prompt: This turn to technological solutions for training caregivers in the face of an inadequate healthcare system is nothing new. At least since the early 1960s, when the country faced a shortage of trained nurses, computer-based education has been touted as an efficient and cost-effective way to patch holes in the nation’s disastrous healthcare infrastructure. Then, as now, the rhetoric of urgency has been paired with the logic of cost savings to make online learning and computer simulations seem indispensable.

Response: But computerized medical education has inevitably represented complex patients through grossly simplified models. Because you can’t fit the diversity of human health experience into a software program, this education has always been oriented around notions of so-called “normal” or “typical” patients. In reality, these “typical” patients turn out to be composites of the sorts of people who hold power in society, particularly well-off white men. As a result, computerized medical education has helped to perpetuate the structural racism and sexism that has long pervaded the medical establishment, as well as our wider society.  Working under the promise that a computer could “dispense information just as effectively, sometimes moreso, than a human instructor,” students in Illinois in the 1960s began the very first experiment in computerized medical education, learning nursing fundamentals on one of the world’s earliest computer networks. Looking back to those students and their computer-based courses demonstrates what is often overlooked, and even dangerous, with techno-care, and why that matters more than ever in our algorithmic age.


Prompt: Crushing Substernal Pain In the early 1960s, Maryann Bitzer was pursuing her master’s degree in educational psychology at the flagship campus of the University of Illinois. The university also employed her husband, Donald, who was using his engineering doctorate to investigate whether computers could be used effectively for education. Throughout the 1960s and into the 1970s, Donald led a team of researchers, including Maryann, in developing a computer network known as PLATO, Programmed Logic for Automatic Teaching Operations. PLATO comprised individual user terminals connected to a mainframe computer and, through the mainframe, to each other. The network went through several evolutions, and by the mid-1970s it included nearly 1,000 terminals around the United States, each with a flat-panel plasma touch screen, with applications including games, instant messaging, screen sharing, and email.

Response: In a decision that ultimately benefited both of them, Maryann focused her master’s thesis on how computer-based education could work in nursing. She cited two motives for her study: a dearth of trained nursing instructors across the country, and the tremendous educational value for nursing students of working with “actual” patients. Using one of the early iterations of PLATO, which employed custom keysets and television-like cathode-ray tube screens, Maryann developed a course on treating heart attack patients. Then she delivered it to first-year nursing students at the university-associated Mercy Hospital. The course imaginatively integrated several components, immersing students in what sometimes seemed, behind the gloss of the new technology, like an actual experience of care.


Prompt: First, the trainee nurse watched a short live-action film on the PLATO screen that depicted a doctor interacting with a patient, a middle-aged man. It was a clever way to present what Maryann described as “the patient’s socio-economic background, his present family situation, and his outlook on life [and] the patient’s past medical history and treatments.” Then, partway through the conversation with his doctor, the man grabbed his chest and was rushed to the hospital.

Response: After this sudden turn, the terminal presented a series of screens that provided the trainee with fundamental information about different diagnostic tests and treatment courses. Then the nurse entered an extended simulation in which she could use the PLATO keyboard to select from a limited menu of options to test out various interventions on the virtual patient and see the results. For example, after administering oxygen or nitroglycerin to her virtual patient, the nursing student could ask the computer to report her patient’s pulse, temperature, blood pressure, electrocardiogram, or other test results. The nursing student could also consult a screen displaying clinical norms, such as the normal blood pressure range, to gauge the effects that her care was having on the patient.  It was a potentially engrossing but also highly circumscribed experience. The scenarios and results of the simulation were limited by the minimal level of complexity that could reasonably be programmed into the computer, especially given the fact that it took roughly forty hours of programming to create just twenty minutes of instruction. The film was designed so that each student could picture a particular living, breathing individual patient for whom they were caring as they worked through the simulation and its twenty-eight questions. But that single patient—a composite of several “typical” case studies—was the sole basis for how the computer was programmed to respond to the nurse’s therapeutic interventions.  This focus on a so-called typical patient—a middle-aged man—both reflected and reinforced the prejudices of contemporary medical practice. A quarter century of research, beginning in the mid-1990s, has shown that heart attack symptoms manifest differently for women than men. For a long time, however, doctors and nurses and PLATO programs did not know how to recognize and diagnose heart attacks in women. For example, Maryann’s course described its virtual patient as having “crushing” substernal pain. But women are much more likely to experience symptoms unrelated to chest pain. If they do experience chest pain, they more often describe it as discomfort or pressure. (Despite our advances in knowledge, women are still 50–60 percent more likely to be misdiagnosed following a heart attack.) Maryann’s course was blind to this clinical reality, and as she went on to expand her PLATO-based nurse training, she helped to inculcate this bias, and others like it, in a new generation of nurses.


Prompt: Virtual Mrs. Dodd Maryann’s experiment with techno-care occurred against the background of significant national investments in nursing. In 1960, the US Public Health Service created a new Division of Nursing tasked with improving patient care, increasing the number of nurses, and ensuring better nursing education. In 1963, the Surgeon General’s office published the report Toward Quality in Nursing which identified, among other problems, too few nursing educators, too few new nursing students, and an inadequate nursing education system. Maryann realized her experimental nursing course could be positioned as an efficient technological solution to these problems, training nurses faster and more cheaply than traditional nursing courses. In 1964, Congress enacted the far-reaching Nurse Training Act, designating the substantial sum of $283 million (approximately $2.3 billion in 2020 dollars) over five years to nursing education. The Nurse Training Act funded the expansion of Maryann’s PLATO project to develop a complete course on maternity nursing and a series of lessons on pharmacology.  The reliance on a single “typical” patient continued. The maternity nursing course focused on the virtual Mrs. Dodd, a secretary. Its twenty-two lessons “emphasized the normal, and presented problems which required knowledge of the normal as a basis for recognition of and action concerning the abnormal.” Students learned that “Mrs. Dodd suffers from many of the common discomforts of pregnancy,” including nausea and swollen feet. And just as it was with the “typical” heart attack patient, the way “normal” Mrs. Dodd responded to therapeutic care was contingent on how PLATO had been programmed.

Response: That programming was based on the standard of care for pregnancy in the 1960s, which was developed for, and applied to, white women—a bias that reinforced the invisibility of Black women to the medical establishment. (At many hospitals, including Mercy, the nurses, too, were overwhelmingly white; according to an archive at the University of Illinois, among the hospital’s hundreds of graduates until it closed in 1970, there were only ever six Black students.) For example, in the PLATO course, nurses monitored virtual Mrs. Dodd throughout all three trimesters of her pregnancy, as well as labor and delivery. But many Black women, then and now, lack sufficient access to and insurance coverage for complete prenatal and postnatal care; nurses exclusively trained to care for patients like Mrs. Dodd are poorly prepared to care for these women. Indeed, in the past few years, prominent Black women including writer and scholar Tressie McMillan Cottom and tennis superstar Serena Williams have called attention to how they and other Black women are dangerously mistreated during pregnancy, labor, and delivery. As Cottom recently wrote in Time: “In the wealthiest nation in the world, black women are dying in childbirth at rates comparable to those in poorer, colonized nations.” Though severely limited, Maryann’s nursing course was nevertheless a success—in part because it reflected the limitations of the surrounding medical establishment. All of the students who completed the PLATO maternity nursing course later passed the Obstetric Nursing portion of the Illinois State Board examinations; the biases encoded in Mrs. Dodd were the same ones written into the exam. During the remainder of the 1960s, hundreds of students at Mercy Hospital School of Nursing and nearby Parkland Community College completed PLATO nursing lessons, thus inscribing the biases into their own care.


Prompt: The Other Pandemic In 1970, to commemorate its seventieth anniversary, the American Journal of Nursing invited prominent researchers to reflect on “Nursing in the Decade Ahead.” Under the title “Computers Have Entered Our Lives,” Maryann declared, “All indications are that computers are likely to play an integral part in assisting members of the health professions to provide comprehensive health care to people.” She has since been heralded as the influential figure who introduced simulations and computer-based courses to nursing education, where they are now widespread.  But the types of sexism and racism subtly enacted on PLATO’s small screens through Maryann’s nursing courses also continues to pervade computerized medical education—even in the midst of a pandemic that is disproportionately killing Black people. In early April, the healthcare staffing provider IntelyCare reported that over 37,000 nursing professionals had completed its COVID-19 online training, and another 310 healthcare facilities enrolled their staff in the course. When I took the course in early July, I discovered that every single patient and healthcare provider in it is white. Similarly, the online training assembled by the American Association of Critical-Care Nurses, which echoes Bitzer’s heart attack course on many levels, has four units that each begin by immersing students in a critical healthcare situation with a “real” patient, all of whom appear to be white.  The exception that proves the rule is a webinar from the American Nurses Association titled “How You Can Have a Direct Impact on Reducing the Devastating Racial Disparities of COVID-19,” which presents the case study of a forty-five-year-old Black man. Released in June amid protests over police brutality and the police murder of Black people, including George Floyd, it is the only online COVID-19 training I’ve seen that even begins to address American healthcare’s deeply ingrained racism.

Response: These virtual trainings are telling instances of the larger systems of racism and sexism that are shaping the country’s response to the pandemic. Black people are dying from COVID-19 at a rate at least six times higher than white people. There is a multiplicity of overlapping and mutually amplifying reasons for this: the doubt with which many healthcare workers treat Black people when they report symptoms; the overall poor quality of care that Black people receive; the cumulative damage of environmental pollution that disproporationaly impacts Black communities; the precarious place that many Black people occupy in our racist economy, especially in the poorly paid jobs that we now deem “essential”; the daily stress of enduring racialized discrimination and violence. As a sign carried by Dr. Jasmine Johnson, who studies maternal-fetal medicine, declared at a Black Lives Matter protest in Chapel Hill, North Carolina in June, “Racism is a pandemic, too.” Computerized medical education could be used to highlight and challenge this pandemic of racism, but only if institutions and society are willing to attend more closely to the specific needs of women and Black, brown, and Indigenous people; devote the resources necessary to creating courses that reflect the diversity of human experience; and overturn the centuries of economic exploitation that leave Black people at the bottom of America’s caste system.  Ultimately, though, no computer program can sufficiently capture the extraordinary range of individual human medical experience, so we also need to invest in the sorts of hands-on medical education that doesn’t seem efficient or cost-effective to policymakers and hospital administrators operating under the perverse logic of austerity. And as long as the medical establishment and medical knowledge remain hostile to the experiences of women, Black people, and other members of BIPOC communities, then no form of medical education can escape these sorts of biases.  Of course, this is true beyond medical education as well. The dramatic rise of online courses—not just in healthcare but across higher education—raises questions that were as relevant in the 1960s as they are today: What are we teaching? Who are we teaching? For whose benefit? For what larger public good?


Prompt: At the university-based outpatient practice where I work as a gastroenterologist, my office overlooks the construction site of a huge new hospital that is slated to open in the summer of 2021. Since taking this job three years ago, I’ve watched the project develop from its steel skeleton to its curving facade, a layer cake of plate glass and copper panels. The aesthetic matches the streamlined, futuristic style of academic megahospitals from Texas to Tokyo, casting the building as a technology that’s every bit as cutting-edge as the microsurgical robots and cancer immunotherapies it’s designed to house. At seventeen stories high and a price tag of $1.5 billion, it’s a tower among towers (including the one where my office sits on the seventh floor), adding to an already imposing skyline of healthcare buildings near the center of Philadelphia, just west of the Schuylkill River.  The new hospital is also visible from two vinyl tents, each slightly smaller than a double-wide trailer, that were recently set up in front of my current hospital’s emergency room in the early phase of the COVID-19 pandemic. With a few dozen fellow doctors, nurses, and medical assistants, I was redeployed there for several weeks in April to test and triage patients walking in with flu-like symptoms—coughing, short of breath, but not quite sick enough to merit a hospital bed. As far as tents go, ours were pretty nice: we had Wi-Fi, some bleach-proof laptops, and sinks connected to hot water hoses that were slung like vines across the sidewalk. We also had carts that were implausibly well stocked with gloves, goggles, masks, and nasopharyngeal swabs. I was grateful for those carts, having heard terrible stories of healthcare workers dying for lack of basic supplies at the loftiest medical institutions of nearby New York City. So long as I was wrapped in the appropriate protective equipment, I was content to sit in a folding chair with rainwater pooling at the edges of our makeshift clinic.

Response: But I was also struck by the juxtaposition between the looming megahospital, glinting in the sun like a freshly landed spaceship, and the humble tents below. It evoked an abiding paradox of modern medical care. Contemporary hospitals are like great machines: each discipline exists in its own silo, outfitted with its own technologies and specialized knowledge, often connected to other disciplines only by confusing networks of elevators, corridors, primary care physicians, and a vast, computerized bureaucracy. The proton-beam radiation equipment used to treat prostate cancer patients in the basement is far removed from the specially-trained neurology nurses working with brain-damaged patients on the floors above, but they meet in the electronic medical record, with its templated notes and billing codes. Critics have long pointed out that the enormity and complexity of this infrastructure, which is designed to literally and metaphorically convey biomedicine’s healing power, frequently leaves patients feeling lost, even spiritually deadened.  The current pandemic adds another wrinkle to that longstanding contradiction. In the face of a widespread threat to our wellbeing, the megahospital hasn’t risen to the occasion. Past social upheavals, such as postwar modernism and the rise of mass production, helped to fuel major evolutions in hospital design. Looking out from my office window at the massive structure taking shape by the river, I wondered whether, after COVID-19 had laid bare the limitations of contemporary megahospitals, these buildings might be refashioned once again.