Prompt: Azad: The tenants unions in Los Angeles and San Francisco already do this kind of research when they’re fighting evictions. And organizers in other cities around the country are making similar tools, drawing from the same resources that we draw from: assessor data, property records, eviction data where possible, and sales data.  With EvictorBook, we’re just trying to make it easier, since tenants are most motivated to do this research when they’re already in crisis. They’re faced with belligerent landlords who harass them in all kinds of ways—ripping out appliances or sending fake legal notices to get people out.  Before we had EvictorBook, we’d start at the assessor’s office doing searches on paper and then look up what we could online. But property ownership networks have gotten very sophisticated. People wring out profits from one city and then move to other cities. So in the process of connecting all the data we’re working with, we’re also trying to map networks of financial speculators and evictors.

Response: Have you heard about any surprising uses of EvictorBook? Azad: There are a bunch of use cases. One is proactively asking: What has this landlord done in the past? Is this landlord representing themselves accurately?  Another is: Is this landlord lying to me about what’s taking place? In California, landlords can do what’s called an Ellis Act eviction, where they evict someone because they say they no longer want to rent out the building. In Los Angeles, there’s a five-year limit with this kind of eviction when the landlord is not allowed to rent out the unit they evicted someone from. But they can move in for a little while and then sell the property before five years are up. There are also owner move-in evictions that operate similarly. EvictorBook could tell you if something like that is the reason you’re being evicted.


Prompt: Do landlords do these kinds of evictions in order to get rid of rent control in their buildings? Erin: Indirectly, yes, because they can rent and sell the units for more money if there’s no rent control. In San Francisco, after a landlord uses the Ellis Act to evict tenants from a rent-controlled building, the building will often then get sold as multiple “tenancy in commons,” which will still have rent control—but then they will get converted into condos. And when that happens, the building loses rent control. That’s one of many loopholes. In short, what we’re seeing with Ellis Act evictions and Owner Move-In evictions is that we’re losing effective rent control, which, in the case of condo conversion, is a nonrenewable type of protection.

Response: Azad: I was thinking we could add a feature to EvictorBook that shows how much a landlord profited from an eviction. We have the sale price before and the sale price after. Erin: Oh, yeah, that would be great to see. Azad: It wouldn’t be hard to do. We have all this data that we’re actually not spending much time analyzing. We know who the evictor of a given building was—and not just who evicted that one unit a few years ago, but looking back over fifteen, twenty years of evictions. That, combined with the networks of LLCs, shows not just ownership structures but also the evictor structures in the history of housing, which is the history of gentrification and the history of displacement in neighborhoods that are financialized. There are so many questions we could use this data to think about.


Prompt: For multibuilding campaigns, tenants have to know what other properties their landlord owns. EvictorBook surfaces that information. Half-Open Data Erin: There are all these public datasets and open data portals that different cities have, but none of those datasets will indicate who corporate landlords or evictors are.

Response: Azad: Or the stories of the people who are impacted. Erin: Right. So the data that’s fueling EvictorBook is technically public, but people have to put zillions of hours into stitching it together for what we need. You would think that cities and different administrative bodies would do this work, but it’s not in their interest. So we’re taking public data and connecting it so that it can be useful to tenants.


Prompt: I remember looking for rent stabilization data when I lived in New York, and the closest I found was this project, AmIRentStabilized.com, which is someone’s personal project to help people do this. The city has that data and could share it with tenants, but they don’t. Erin: Totally. Azad: And New York is a special case where there are different levels of rent stabilization and rent control, and the property owners know the status of their building, but it’s not easy for tenants to look up that information themselves.

Response: The website walks you through the process of contacting a city office, which is then supposed to mail you the answer, but if they don’t, you’re supposed to set a calendar reminder for yourself to follow up with them! Azad: That’s because these institutions are beholden to property owners who fight tooth and nail for data about them to remain inaccessible. And with open data, as soon as the data gets politicized, it gets pulled. We just saw this in Chicago, where there were some open datasets about policing in the city—several reports were written about them—and then that open data got removed. The neoliberal fantasy that motivated the open data movement in the first place was like, “If you just open the data, people will make great business products out of it!” But it’s proven to be much more complicated.  EvictorBook shows historical eviction data for a given building—data that’s otherwise difficult to find and piece together.


Prompt: It’s also a ton of ongoing work to maintain that data and keep producing clean datasets. We want to be very transparent in EvictorBook. We’re not just saying: this person might own this property. We’re saying: there was an eviction here, this person owned the property during that eviction, and here’s why we’re sure of that. A ton of due diligence goes into that.

Response: We’ve talked a lot over the course of this project about how we can avoid replicating some of the horrible data practices of property tech. Landlords have blacklists on prospective renters that they create by doing a regex [Ed: short for regular expression, a way of defining a pattern and then finding matches that fit the pattern] on five characters of a last name, without any other verifying information. That’s a great way to block large swaths of entire demographic populations from renting. We’re being extremely careful about that kind of thing.  Tell me more about these lists of renters based on five characters of a last name. I usually think of landlord tech more as surveillance cameras in buildings.


Prompt: Azad: It’s that too. Erin: Tenant screening has been around since the mid-1970s and has experienced different booms, such as a big one after 9/11 and another one after 2008. It’s getting more and more robust as an industry, weaving together past eviction history with any criminal record history that someone might have, along with credit reporting. There are so many bad data practices within each step. Of course, people are being wrongly criminalized at the outset, but also, oftentimes, these screening bureaus only look to see if a tenant has appeared in any kind of housing court—not necessarily what the outcome of the case was. So someone may have reported that their landlord was abusive, which gets their name registered in some database because they filed a complaint. It’s a mess.

Response: Azad: We talked to eviction lawyers who were shocked that those lists were out there. We assumed that they knew, but they were like, “No, there is no central repository.” There’s just a patchwork of data on people who have been evicted. I think tenant screening companies literally just sell CSVs back and forth.


Prompt: Erin: Different states have different policies about this. A few years ago, California passed a law that’s supposed to protect tenants from having their data collected by these screening companies. New York just last year passed a law that bans tenant blacklists. But apparently it’s still happening in both California and New York despite these laws. More broadly, there’s not a lot of regulation or enforcement around how these companies get data. In New York, LexisNexis sends its own people to housing court to take photos of the computers with all the eviction records. Then they bring those back, standardize them, and sell the data to tenant screening companies. Really weird things like that! The surveillance you mentioned is also part of the property technology, or proptech, umbrella, which involves biometric data and all kinds of other tools and practices. We have been having a lot of conversations with other organizers about how to ensure that we don’t put data out there that can be used against tenants.

Response: That gets to a security issue: there are housing justice groups already using EvictorBook, but it’s not publicly available yet. Can you talk about how you’re thinking about whether or not to make it public? Azad: We’ve been thinking through worst-case scenarios: how could this data be used in a way that we didn’t plan for? What if the data was hacked? The eviction data is something we have access to that most people do not. We’ve gotten it through relationships with the courts, housing boards, and tenant organizers. So the raw data itself will probably remain restricted. But the rest of the site will be public and usable.


Prompt: Erin: We’ve been weighing the risks of putting it out there—there’s always more verification of the data that can be done. On the other hand, there are risks involved in not putting it out there—it could be useful to people organizing rent strikes right now, particularly with Covid.  Anti-eviction Constellation Chapters are a relatively new thing for AEMP. AEMP used to be limited to San Francisco. As the group grows, how do you keep the focus on housing justice that the original chapter has? Erin: One of the things that I’ve always valued about AEMP is that people come in with such different backgrounds. Probably half of the people in the collective are doing narrative-based and storytelling work. There is a growing crew that’s bringing software skills, which are necessary—but we do work collectively to stay as far away as possible from Silicon Valley tech culture in our processes, in how we conceive our projects, and in how we work with community partners. The housing justice politics undergirding our work are first and foremost. Many people in the project are also in tenants unions, show up in different coalition meetings, and/or participate in actions.  We’re also a young organization. When we began in 2013, our group was housed in the San Francisco Tenants Union, which has been around for decades, as have many other orgs we’ve worked with. When the LA and New York chapters started, people in those places did a lot of outreach to figure out what kind of organizing was already happening in their cities.

Response: Azad: In every chapter, there’s pretty tight integration with tenants unions. I know in LA there are four or five chapters across the city, and we have people going to meetings at most of those. We see ourselves as beholden to that movement; it’s built into our projects, the community agreements we’ve written, and the onboarding. We’re very clear that we’re not just building whatever tech.  The Anti-Eviction Mapping Project is always juggling collaborations with multiple other groups. At the same time, both of you have mentioned the zillions of hours that have gone into EvictorBook alone. How are you sustaining these efforts in a volunteer project? Azad: I don’t know how Erin keeps it all together. I’m on two projects and it’s a full-time commitment.


Prompt: Erin: You do so much, Azad. He’s working on both EvictorBook and the COVID-19 Global Housing Protection Legislation and Housing Justice Action Map, which tracks tenant protections, housing justice actions, landlord retaliation, and soon will include tenants’ oral histories since Covid. It’s an interesting moment because, for a while, we had these three chapters, and each chapter was working on its own projects, so I don’t even know a lot of what’s happening in places where I’m not there, going to those meetings. I know in the Bay, there’s a thirty-minute film that’s an homage to tenant organizing that weaves together three struggles: the anti-Google fight in San Jose, the fight for rent control in Santa Rosa, and the fight for Aunti Frances’s home—she’s a former Black Panther who was evicted from her home in Oakland a few years ago. Our Counterpoints atlas will come out later this year with PM Press. That’s a project that many, many people have been working on for almost three years. Probably well over a hundred people contributed to the atlas, some affiliated with AEMP and others not. There’s also the Black Exodus zine that came out last year that tracks Black experiences of gentrification and resistance in San Francisco. I’m sure Azad knows about a lot of projects in LA that I don’t know about.  With EvictorBook, we started this interchapter collaboration model that more of our work is taking on, since Covid means that we’re all meeting online and not in person anyway. We’re having interchapter meetings every month whereas we used to have those every few months. But, yeah, it’s all volunteers, and we’re always trying to find ways to create more sustainability and be better organized. People are dedicated, and it somehow keeps going.

Response: Azad: I think it’s partly that these are decentralized projects, and we’re just providing a framework. The framework is: How does this relate to housing justice? You’re not just making this for your own curiosity or to scratch your own itch; you can do that by yourself. So what is the nexus between how we see our role in the housing movement and the kinds of group projects that people are willing to take on?  When the LA chapter started, we would pair with another organization in the movement and they would say, “Help us look into this.” More recently, it’s become less of that model. Instead, we have a north star—it might be a little vague—and within that project, people are decentralized and empowered to work toward the ends they define. That’s the post-and-beam structure that AEMP provides.


Prompt: What would you say is the north star of all of these projects? What do you see them as building toward? Erin: This is not an AEMP-affiliated project in any way, but we’re really inspired by the Moms for Housing movement, which has shifted the conversation in the Bay Area. Four Black mothers in Oakland who had been unhoused organized to reclaim a home that was sitting vacant. It was owned by Wedgewood, which is one of these big investment companies with many LLCs throughout California. They were asking: Why should there be this vacant home owned by this corporate landlord when there are so many people who need housing now? The mothers helped create a huge movement in Oakland that inspired people in LA and beyond to do similar organizing. That spirit, that politic, has all sorts of afterlives, particularly in this intense moment where eviction moratoriums are ending while there’s still a pandemic going on.  Azad: What’s the north star? That’s a question that I think everyone who does work like this asks themselves. Is it about resisting, is it about building toward something new, is it about throwing sand in the gears? For me, it’s a mix of all of the above: community land trusts, decommodified housing—and let’s also repeal the state proposition that bans new public housing from being built in California! But more broadly, I think the idea is to keep making constellations. We’re providing north stars for others, and they provide them for us.

Response: How did you first get involved in open source software, and what drew you to it?  Before there was open source, there was free software. I generally had this late-1990s, early-2000s understanding that, within the technology world, there was free software, which was often literally free, and then there was Microsoft stuff that cost a lot of money. I grew up poor and it was financially very difficult getting through college, so just in terms of access, I thought, “This Linux thing that I put together from various parts? This is good.” I went to the University of Washington in Seattle. Microsoft land. My experiences were shaped by both my economic status and the ways I saw Microsoft impact the university. It all funneled me to the free software, and, later, the open source movement.  I got into the community side of open source through volunteering at Free Geek for a couple of years, which is a computer recycling facility and educational institution. They have a great founding story about starting the organization after seeing a computer monitor that someone had thrown into the Willamette River.  Also, they give you a computer that runs Linux if you volunteer there. I graduated from college during a recession, so it took me a long time to find work and my computer died in the middle of that process. I figured I’d put in some volunteer time and get a computer. That’s what brought me into this local community of user groups, where people were teaching each other about programming. Most people would call these “meetups” now, but the user groups hosted at Free Geek tended to focus on a particular technology, like Perl or PostgreSQL. That community supported me getting into this career. So, it made sense for me to find ways to support other people too.


Prompt: At some point, you left Seattle and went down to Portland. You got involved in the Portland open source community in a big way, planning multiple conferences over the course of a decade. What drew you to that work?  I just kept asking questions. The question that brought me to Free Geek was: How do I get a computer? The question that brought me to user groups was: How do I learn how to write software for it? And then: How do we cross-pollinate the different user groups? The answer was to host meetups and organize conferences. How do we keep track of all the events we’re hosting and resources we’re sharing? In 2008, I ended up building Calagator, which is an open source calendar app that’s also kind of a wiki. I’m still the maintainer.  It's always been like that: We need this thing. How do we get it? How do we make it? Where does that take us next? For the past decade, it’s always led you back to open source. I'm curious what your thoughts are on the state of open source today and the phases you've seen it go through.

Response: Open source is not one big thing; it is a giant umbrella for many different activities and goals. For a long time, Linus Torvalds said that the goal of the Linux project was technical excellence. The technical excellence perspective has meant that open source is the base layer of most technologies that we work with. Linux is in everything. Developer tools, web servers, our programming languages are all open source or built on open source components. Very little of our technical stack is proprietary. That's part of what drew corporations to open source. That’s also what drew me to open source: it provides low-cost solutions for people.


Prompt: What has become more complicated over time is the ideological piece. I’ve written about reframing open source, so that it values the people who make it more than anything else. The lens we don’t apply nearly enough is: How do we build technology in a way that is beneficial to people? How do we make software for what we actually need? Software that supports us, teaches us, builds communities, and solves real problems. We need to talk about this from a community perspective, from a human perspective.  Technical excellence cannot be the primary goal. I do not care about code quality, or how fast the process is, or whether it scales. Where we need to innovate today is around community safety. Many of the technical challenges that we work on as developers have had solutions for decades. I think that we can take what has worked for us from that version of open source and throw the rest of it back out there.

Response: My answer would have been much tidier a year-and-a-half ago, but my perspective has shifted from thinking about the open source community as the center to thinking about where open source sits within the rest of the world. It doesn't give me as easy of an answer. Mind the Gap You founded The Recompiler in 2015 to feature writing on technical topics like DNS and floating point numbers. The website describes the magazine as a feminist hacker zine, whose tagline is "Technology for the world we want to live in.” How does The Recompiler and the larger Recompiler Media project fit into that?  I have to be honest here and say that the question The Recompiler was answering for me was not: How do we build better technology together? Although that is a great, true mission statement. The question for me was really: How do I continue to work in technology—after a certain point in my career, knowing what doesn’t work, knowing that this industry continues to marginalize people and exacerbate power differentials?  Is there a specific experience you were thinking of? It sounds like a shitty workplace.  It was, but it was also burnout. It was the mid-career developer experience of someone who's not a white man. So many conversations I’ve had are about that experience, and they’re almost always framed as: Do we stay or do we leave? And I thought, well, what else, what’s another option? That's really how The Recompiler came to be. Prominent writers and speakers in the industry kept effectively saying that there are no women in technology. But I knew women in technology, I knew people of color in technology, I knew people from lots of marginalized groups in technology. So the Recompiler projects were about creating a platform to reflect that we already have technical experts representing a lot of different kinds of people.  Across the Recompiler Media projects—the newsletter, the podcast, the magazine, the blog—you cover a wide variety of subjects: technical deep dives, police brutality in Portland, how surveillance technologies work at the code level. How do you see those all fitting under the same umbrella?


Prompt: It goes back to asking questions and following them, whether it’s operating systems or legacy software or what the police are doing in Portland. I always wanted the deep dives to be what distinguished us. Really good, easy-to-understand explanations of technical topics that everybody bullshits about.  You’re interested in demystifying.  Yeah. We need to understand how things work for ourselves. And, if you’re asking questions about how technology impacts people’s lives, policing and surveillance keep coming up. Palantir, facial recognition, who had access to what databases after the 2016 election—there was no way to avoid those topics. So, that's what brought the Portland protest blog into The Recompiler’s wheelhouse.

Response: Can you talk about the organizational structures of The Recompiler and how you find contributors and collaborators? I usually give my title as either publisher or editor-in-chief, depending on who I'm talking to. Basically, I pay the contributors. The only person who does not generally get paid is me. I would love to be doing this full-time, but this is why I've come back to having a day job. I did do The Recompiler full-time for a couple of years, but funding-wise it's better for me to have a software engineer job.  That is also part of Logic's funding model.


Prompt: It helps. In terms of finding contributors, we've done an open call for every issue. I encourage people to put stuff in and people come to me asking if their idea would be a good fit. That's how we ended up with the Responsible Communication Style Guide. My friend Thursday Bram had always wanted to put out a guide like that. I thought it was a good idea and that people would use it, so we did it.

Response: With the newsletter, the person who had done it originally wanted to step away because talking so much about all the ways that technology harms people got to be really draining. I started asking around and somebody introduced me to Margaret Killjoy, who does the newsletter now. She makes most of the decisions on that, and she’s brought a more leftist, anarchist focus, which I think is very appropriate for the moment.  Since first reading Margaret in the newsletter, we've been listening to her anarchist prepper podcast in our house. Did you ever think that The Recompiler would be one hop away from an anarchist prepper podcast? One of the skills that I bring is that I'm always looking for the gap—what do we need that nobody is doing? And I’m always thinking, “What if I grab another person and we make something to fill the gap?” That’s how all these projects start, in this fractal way, where we're going along and then I see an opportunity halfway through and branch off. What I hope is that we end up with a whole organism of projects that are supporting the different interlinked needs we have.


Prompt: No Bosses, No Knife Missiles I want to switch gears and ask you about labor organizing and union busting at NPM. I found a GitHub gist that you published in 2015, titled “A Feminist Hacker's Guide to Labor Organizing,” a year or so before Trump’s election sparked the current wave of white-collar tech worker organizing. In 2015, it was a national press story that Googlers were anonymously sharing their salary data in a spreadsheet, and #TechWontBuildIt was still years away. Do you remember making that gist? Wow, yeah, I do remember that. That was after a conversation I was having with someone in a user group. I wrote it down, sent it to the list for the group, and haven't looked at it in years. I wonder if there’s anything I would disagree with now.  It's pretty solid! Can you talk about why you were thinking about tech worker organizing in 2015?  Before people were talking about Trump and how technology would be used under his administration, I was concerned about power differentials within tech: the ways that tech companies drive developers to burn out; the fact that Silicon Valley companies saw us in Portland as a source of cheaper labor; the fact that you can't make your boss stop being racist, but you can create consequences for your boss being racist.  I wrote that gist because we were talking in the user group about all the things that hadn't helped us. The employee resource groups that companies created to try to make us feel heard, HR, all these trainings that were like, "Here's your bias," and then everyone says, "Yup," because bias does not go away when you learn about the existence of the bias.  There was this real frustration about how things weren't changing. We had been in the industry long enough to have seen some efforts that went nowhere, and not because we didn't try very hard. We became educated about a lot of things, and then we told other people, and then they still didn't do what we needed them to do. I just got to this place where I didn’t want to keep nicely asking: “Please stop being racist, please stop being sexist.” I wanted to do something that we hadn't tried yet.

Response: Labor organizing is a solution. It helps us leverage what we have, which is people—multiples of people—against these institutions. But it requires that workers are informed and talking to each other. In many ways, it’s like organizing an open source project: we have people, we have a need, how do we share what we know with each other, how do we build something together? How did that start at NPM?  My organizing at NPM happened by accident. Right before I joined, the company brought in this CEO who was the stereotypical guy that you hire so that the company can finally make money for the investors. But the company had attracted people whose ideals were at odds with just cashing out at any cost. There had also been unaddressed burnout issues before the CEO joined, and he made them worse because he was so terrible to the people who had put in so much work to get NPM to that point. Yet we were encouraged to talk about it all: about burnout and retention and the company's new focus.


Prompt: I was in a team meeting at the company all-hands, which was an offsite, but I was one of two people who was there over video. People were saying things like, “Well, you know, if we had HR, then we'd have somebody to help with some of these management issues. And if we could get them to do this, then we'd have that.” And I was like, “That's cool, but we could also unionize.”  I was having this really awkward experience over video, I was cranky, and, as I mentioned, none of my previous experiences led me to believe that we were going to be able to make incremental institutional change in the way that some people wanted. so I didn't think that it was a particularly outrageous thing to say. I thought some people would agree with me, although I did have a moment of doubt afterwards when it occurred to me that maybe everyone thought that was a terrible idea and there was nothing I could do about it because they were in a room together and I was just there on a screen. But, in fact, there were people who were thinking, “That's what I wanted to say. She just said it.” Were you friends outside of work already? Because I'm guessing you weren't doing this in the work Slack.  After that conversation, two of my coworkers were trading phone numbers and talking about meeting up later. So over the Zoom call, while the bosses were out of the room, we all exchanged numbers. Our initial organizing group came out of that.

Response: Now that everyone is a remote worker, no one has those in-person opportunities. But what you can do is say, “Hey, I'd like to talk to you about how things are going.” Companies all have their own ways for workers to connect with each other. And then over Zoom you say, “A few of us are talking about our concerns in this other place—would you like to join us? How are you feeling about what the CEO said or that policy change or whatever?” Just ask. And then you get off the Slack. No Slack DMs about how you're going to unionize! Once we had our organizing group at NPM, people started doing something that’s really common in these groups. They’d say, "This is really bothering me." And then other people chime in and say, "Yeah, that really bothers me too." Or, "I think I'm getting underpaid. What are you getting paid?" Or one person voices concerns about the lack of trans healthcare, and then someone else would offer to ask about it for them. That kind of thing often happens automatically. I'm not just talking about NPM here. But people start having these conversations about what is actually happening and agreeing with each other. Even if it's just a solidarity forum, that's valuable.


Prompt: What really grinds us down in tech is feeling like we're not heard or seen. We're told that everything is fine, that everyone loves it here! The solidarity groups can be validating. And people are motivated and impacted by the kinds of topics that come up there, so they want to teach each other—about forms of marginalization or using inclusive language or organizing in general.

Response: Something that’s been interesting is that when I asked people what brought them to the organizing group, they often responded that they had some experience of organizing from another area of their lives: they grew up in a union household, they organized in college, they had a family member who talked about organizing—more tech workers than you would think. And that’s a stepping stone for them.  At some point, you were illegally fired from NPM.


Prompt: Rather quickly, yes. Because I made that comment in front of my whole department and it didn’t take long to make its way back to the CEO. They didn't know who all was involved, but they got rid of who they thought the troublemakers were. That ended up being a sizable part of their open source expertise.  One of the other people who was fired said that they were going to file a National Labor Relations Board complaint. I'd never even thought about it. I just had to find another job immediately because I was coming out of doing The Recompiler full-time, so I didn’t have any buffer left. But this person wanted to try so I said, “Okay, let’s try.” The combination of the firing and the NLRB settlement negotiation was honestly one of the most stressful things I've ever gone through. I was job searching and then starting a new job and worrying that I was going to spend my fortieth birthday testifying. There were so many calls about what we were going to ask for and what we’d accept and what was happening with the negotiations. I'd be on the phone wandering through Muji, totally overwhelmed, staring idly at a houseplant. And NPM’s CEO was a real dick to us. When we thought we had agreed to a settlement to the NLRB complaint, he reneged on the agreement.

Response: I learned a lot, like that I don't ever want to go through that again without a union filing the complaint for me, which is how it usually happens. But the Oakland NLRB office was great.  I read in some of the reporting that NPM management eventually settled with three of the workers who were fired and who weren’t managers and that, as part of the settlement, the NLRB made the company remind all the workers that they are allowed to organize.


Prompt: Yup. Reminding people of their rights turns out to be one of the stock things the NLRB does in these kinds of settlements. We were really into it. We also received back pay for the period between our firing and the settlement, and we received a few weeks of additional pay that was similar to the severance agreements we turned down in order to be able to talk about what happened.

Response: As we wrap up, can you talk about where you're at politically now and how you see that as interwoven or not with your professional work as an open source developer? We are living through an extreme moment in history. The things that felt relevant even a couple of years ago don't have the same relevance or urgency now. I was focused last year on different kinds of ethics clauses in open source licenses. I’m not doing that right now. I've spent my summer watching people be beaten by the police. I’ve heard politicians say again and again that tear gas is bad, but then there is still tear gas. I’m one of many people who have been radicalized by this summer in Portland—by what the police and the mayor have done. I was already a lefty, but it's refocused things for me.


Prompt: So I'm still thinking about labor and open source and access in tech, but there are some life or death issues in front of us that software is not going to solve. The technology industry has landed at this point where it is not separable from prisons, policing, and surveillance. If companies are there to make the profits, do the launch, and get the return on investment, they are going to be doing this work. Look at policing as a budget line item in our cities, even small cities. Look at the military contracts, the intelligence contracts. That is where the money is. Companies say, “Oh, well, we don't work with ICE; we only work with DHS!” Or “We work with Raytheon, but not on the knife missiles!”  We have to get rid of ICE. If we don't want Palantir helping DHS identify immigrants to put in cages, we have to get rid of Palantir and DHS. We have to abolish these systems and, when we do, the tech industry will have to find a way to make money some other way.

Response: Amazon is a huge and complex organization. How should we think about it as a whole? Amazon is an opportunistic corporation. It invests in businesses where we think we have a competitive advantage. In general, Amazon thinks of itself as a technology company. So we put the technology first, whatever the product is that we’re selling. And we believe that because we have so much talent and so much capital, we should be able to use our technology advantage to dominate any market that we decide to enter.  What were its origins? Why did Amazon start off as a company that sold books on the internet? In the mid-1990s, the internet was widely seen as a replacement for the library—the library 2.0—so figuring out how to buy books on the internet felt like a natural next step. A little later on, you could see some of that same spirit living at Google through the Google Books project, which was an enormous undertaking. They put a hugely disproportionate amount of resources into it. Amazon’s ultimate goal was similar to that of Google Books: to digitize all of the information in the world’s books and make it available universally, because that was the promise of the internet.  Jeff Bezos studies other “great men” in history and imagines himself to be a kind of Alexander the Great. There's even a building on the Amazon campus called Alexandria, which was the name of one of the company’s early projects to get every single book that had ever been published to be listed on Amazon.


Prompt: But there was also a more practical reason. Books are ideal because you can stuff them in a box. They’re relatively cheap to ship. Also, they’re easy to protect when shipping. It’s difficult to damage books. From the beginning, Amazon sold physical things. That meant its business evolved very differently than that of Google or Facebook, which make their money by tracking people around the internet and using that information to sell ads.

Response: Right, Amazon is not primarily an ad-driven platform. Although it does have a subsidiary, A9, that’s in the online advertising business. But A9 is not a top moneymaker. The top money makers for Amazon by revenue are the retail side, and AWS. Why did Amazon get into the cloud computing business in the first place? What was the original impetus? Amazon wanted to explore the possibility of selling web services because they realized most other firms weren't doing a terribly good job of it. From the start, startups flocked to AWS because we saved them a lot of time and effort. Once AWS had the startups hooked, it was easy to start selling to large businesses—the “enterprise” market—because they envied how well the more technically sophisticated startups were doing.


Prompt: That was good for us, because big companies are more lucrative. But they also have stricter security requirements. They tend to be in mature industries that are more heavily regulated, and regulators care about how they’re securing their data.  And that’s where you come in.  For a long time, security hadn’t been a big focus at Amazon because the data being collected—what books people were ordering—wasn’t that sensitive. It's not information most people were concerned about anybody having. We had to have a way to secure credit card information to make online transactions possible. But we outsourced that.  After AWS got started in 2006, security became a much bigger concern. Amazon realized how important it was to its most lucrative customers. These days, the company takes security extremely seriously. I think you'd be hard-pressed to find many nation states that have as sophisticated a security approach as Amazon does.  Security is mostly about making yourself a difficult target. It’s like that joke where you go hiking with your friends and a bear attacks you. You don’t need to be faster than the bear; you just need to be faster than your slowest friend.

Response: Big companies have traditionally operated their own data centers. Was it hard to make the case to them that they should move to the cloud? They might feel more secure if they’re doing everything themselves. They might, but ultimately the security standards of their data centers are always going to be lower than those of a cloud provider like AWS. A cloud provider has many tenants and they can have economies of scale that let them have more sophisticated security systems than someone fully managing their systems in-house.


Prompt: Also, if you're a company that's operating your own data center, you're responsible for 100 percent of your security—infrastructure security, transit security, perimeter security, everything. If you move to the cloud, Amazon is responsible for at least some of that. So when you use AWS, part of what you’re paying for is security.  Right; it’s part of what we sell. Let’s say a prospective customer comes to AWS. They say, “I like pay-as-you-go pricing. Tell me more about that.” We say, “Okay, here's how much you can use at peak capacity. Here are the savings we can see in your case.” Then the company says, “How do I know that I'm secure on AWS?” And this is where the heat turns up. This is where we get them. We say, “Well, let's take a look at what you're doing right now and see if we can offer a comparable level of security.” So they tell us about the setup of their data centers.

Response: We say, “Oh my! It seems like we have level five security and your data center has level three security. Are you really comfortable staying where you are?” The customer figures, not only am I going to save money by going with AWS, I also just became aware that I’m not nearly as secure as I thought.  Plus, we make it easy to migrate and difficult to leave. If you have a ton of data in your data center and you want to move it to AWS but you don't want to send it over the internet, we’ll send an eighteen-wheeler to you filled with hard drives, plug it into your data center with a fiber optic cable, and then drive it across the country to us after loading it up with your data.  What? How do you do that? We have a product called Snowmobile. It’s a gas-guzzling truck. There are no public pictures of the inside, but it’s pretty cool. It's like a modular datacenter on wheels. And customers rightly expect that if they load a truck with all their data, they want security for that truck. So there's an armed guard in it at all times.  It’s a pretty easy sell. If a customer looks at that option, they say, yeah, of course I want the giant truck and the guy with a gun to move my data, not some crappy system that I develop on my own.   Wow.  There are also specific security services that AWS sells, such as Amazon Inspector. Amazon Inspector is a tool that will audit all of your configurations for AWS, and will provide recommendations about how to change those configurations.


Prompt: When you make a connection to a server, that connection is made over a specific port. And there are some ports that nefarious people might sniff to see if they’ve been left open because they’re frequently used for the management of that server. And so Amazon Inspector might say to you, “We have scanned your server and detected that this port has been left open and you're not using it. Do you want to close this port to prevent people from trying to connect to it?” Or it might say, “These two servers that you have are communicating to each other in a format that is easily eavesdroppable. We recommend that you use at least version X of the connection software that will plug some security holes.”  If you're a competent system administrator, you should have done all that when you configured your system. But not every system administrator is competent. If you’re the sysadmin for some, you know, insurance agency, what do you give a shit? You live in Sioux Falls. Why would you care about cloud security? You don't have to bust your ass because you live in a small-town market where you know everybody and you’re never going to be out of a job. A lot of companies that are headquartered in remote areas don't have particularly sophisticated IT teams. So they’ll pay Amazon to do security for them.

Response: You mentioned that the most lucrative customers for AWS are large companies in mature industries, which tend to be more heavily regulated. How does AWS help those kinds of companies meet their compliance obligations? Certain institutions and industries are regulated more than others. Take the healthcare industry. Hospitals and health insurance providers are bound by the Health Insurance Portability and Accountability Act (HIPAA). There are a lot of regulations about how data can be stored, how long it can be stored, and what types of consent are required. It can be very difficult to implement a system that is fully compliant with HIPAA, but AWS has products that can help with that.


Prompt: Same thing with the General Data Protection Regulation (GDPR), the European privacy law. It requires organizations to handle personal data in very specific ways. One of the new rights that it creates is the “right to be forgotten.” So if you’re a company that does business in Europe, you’re going to need a way to fulfill requests from customers who want their data deleted. AWS can produce a report that says, “Yes, that data was deleted; we can no longer access it.”  So compliance can create lucrative business opportunities for AWS. Presumably those opportunities will grow, with new privacy regulations like the California Consumer Privacy Act, and more regulations expected from the European Union around data and AI.

Response: AWS is a money machine, basically. Today, retail is something like 70 percent of Amazon's revenue. But AWS is 70 percent of the company’s operating profits.  One of the reasons why the antitrust people are looking at Amazon is because Amazon is using highly profitable businesses where it has a really durable advantage in order to subsidize losses in other divisions that it uses to capture market share. Without an organ similar to AWS, a competitor like Walmart has to lower prices below the level of profitability to remain competitive. And they can only sustain those losses for so long.


Prompt: What’s an example of a division that AWS subsidizes particularly heavily? Prime Video, for one. Jeff loves Prime Video because it gives him access to the social scene in LA and New York. He’s newly divorced and the richest man in the world. Prime Video is a loss leader for Jeff’s sex life. A Really Big Deal We’re now many months into the COVID-19 pandemic. How has work changed for you?  The first thing everybody noticed was conferences getting canceled, and everybody was like, “Wow, I guess COVID is a really big deal!” Then we emptied out all the buildings. There was a bifurcation of the people who work in corporate and the people who work in the fulfillment centers. The white-collar folks are fine because we can just work from home. But there was a huge internal drive to make fulfillment centers safe.

Response: What was the outcome of those conversations? I know there have been a number of collective actions among Amazon warehouse workers around the issue of safety during the pandemic. At the beginning of the pandemic, management brought in some consultants and scientists to analyze how disease is spread. We had one week to figure out what we were going to do and then three weeks to execute. Now there are temperature reading cameras. You can't go into Amazon buildings if your temperature is elevated. They figured out how to reduce touch as much as possible. They implemented ways to scan badges at a distance and ways to use barcodes instead of near-field communication readers. They also created a testing regimen.  There was initially no way to get tests but because of our industrial might and muscle, we were able to identify a vendor, so we worked with them to do tests for warehouse workers. I'm not sure how often those tests occur. And it’s not like we test for a whole slate of things. A lot of the warehouse workers are older people who are out of the traditional workforce and find it hard to get back in because they can't retrain or they don't want to, or the job they used to do doesn't really exist anymore. A lot of them don't work enough hours to get health insurance. So if you have cancer and you might die from your cancer, we won't help you get treatment. But if you have this infectious respiratory disease, we want to know.


Prompt: I actually don’t know if the steps that have been taken around COVID have made things worse, or whether they have improved the warehouses. Internally, people say, “Oh, we’re probably better than our competitions, or other warehousing and logistics companies.” But I don’t know if that’s the case. There were other challenges at the beginning of the pandemic too, right—with supply chains for instance? Supply chains all across Amazon were definitely impacted. It was difficult to get hand sanitizer. It was difficult to get cardboard boxes.  We got lucky in the sense that the beginning of the pandemic overlapped with the Chinese New Year. So we had already accounted for some slowdown, because we expected the Chinese New Year to impact timetables anyway.  Overall, though, it seems like the pandemic compounded all of Amazon's advantages and significantly reduced the impact of all of Amazon's weaknesses.  How so? The crisis advantaged large purchasers. The more scale you have, the more buying power you have, right? If you're a factory, the way that your distribution of customers is set up is that you have one or two customers that make up 80 percent of your business and then a ton of customers who make up 20 percent of your business. So if your production is down 30 or 40 percent, you say, “Okay, I’m going to have to cut out all of the small vendors and only focus on the big partners.” That’s what happened. Amazon was able to get preferential distribution of shipments when smaller companies were totally sold out.

Response: And there’s been so much e-commerce growth in general during the pandemic. The question on everybody's minds in retail is: can Walmart and Target use their local distribution infrastructure to get packages to people's doors faster than Amazon can? Walmart and Target’s only advantage is their physical stores. When the stores are closed, they can be used as distribution hubs. You can pick things up close to where people's homes are and deliver them. Buying Whole Foods gave Amazon the opportunity to reach, you know, 80 percent of the 1 percent. But vast swaths of America aren't reachable by Whole Foods.  The rumors that I hear, both internal and external, are that we're very seriously interested in acquiring post office real estate. The reason why the post office is valuable to privatize is because of their real estate holdings. They have great real estate in every downtown of every city in the United States. Amazon may be interested in buying all of the post office locations, and we have the cash to do it. So why not?  The other week we announced we're hiring one hundred thousand more workers again. We're expanding dramatically across the board, in part-time and full-time, at corporate and retail and fulfillment and logistics and devices and distribution and all the various pies we have our fingers in.  How about AWS? Are you growing the same way? Some of our customers obviously saw a dramatic decline in their income. Some large customers in the hospitality industry and the retail industry negotiated substantially discounted rates or got large one-time credits.  But in general, COVID did convince a lot of companies to accelerate their cloud migration, because if you're an organization that has your own data center, chances are that you now have to implement a bunch of safety guidelines and restrictions. Amazon just operates at such a scale that we can do it better and more efficiently.  For instance, a lot of security companies throughout the industry were impacted because suddenly their secure facility had entry rules. Only one person could be in there at a time. But in the security technology industry there are a lot of processes that need two or more people to be physically present. So that made it impossible to use a lot of the secure facilities because the guidelines were in conflict. It was kind of a Freaky Friday moment where everybody realized that, in the context of a pandemic or a natural disaster, these procedures they created to make sure that their facilities were safe were actually preventing them from following security best practices.


Prompt: Customers who were experimenting with a small presence in AWS, who had maybe kept their own data center for security purposes, freaked out. They were like, “Wow, we really need to move to the cloud quickly because they can do all these things for us that we can't do on premises now.”  Besides the challenges of dealing with social distancing, are you seeing new cybersecurity threats in the COVID era? The security threats that are emerging now are the same as ever. They're just more intense. 80 percent of security problems are petty cybercrime. And that’s what’s going up.

Response: Why? Increasingly, large tech firms use people in developing countries as a disposable white-collar workforce. Smaller shops do too. A lot of startups will have one CTO in the Bay Area, and then they'll have their whole development shop be in Ukraine or Romania or something.  But when funding dries up for startups and companies have to shutter, then all of their digital operation overseas is cut loose. And the people who lose their jobs go into cybercrime. They think, “There's no other options for me. So sure. Let's do it. Lock and load.”  A cybercriminal can also get paid by a competitor to expose data, or to change the configuration so the data is exposed publicly to make that company look bad. The Capital One breach in 2019 involved a former Amazon employee, actually. Capital One suffered a huge embarrassment in the press. But unfortunately, as with the Experian data breach in 2015, the Capital One incident showed that the markets are very forgiving of data breaches, because the people who are most victimized by them are poor people who have no idea how to control their data anyway, and didn't even know what it means to have their data breached.  Companies don't like to have their whole ass be shown that way. It's a lot of egg to get on your face. But in the end, the market is learning that massive data exposures are not that bad of a problem unless Congress comes calling.  Unlocking the Last Foot  So you work in cybersecurity and that’s clearly a major focus for AWS. But as we talk about this, there’s another sense of the word “security” in the back of my mind—home security. This is an area Amazon is now getting into with Ring, the internet-connected doorbell.


Prompt: I wasn't involved with that acquisition. But what I've heard is that our investment in Ring was initially about wanting to combat package theft. The retail side of Amazon is basically a logistics company. We have a distribution infrastructure that we chop up into different segments. “First mile” is from manufacturing to distribution. “Middle mile” is from the first distribution center—a warehouse—to the second—the place where a package will get delivered from, like a postal depot. “Last mile” is from that second distribution center to people's front door. And one of the major problems with the last mile is package theft.

Response: But that wasn’t the only motivation with Ring. More broadly, Amazon’s smart home projects are also aimed at unlocking the “last foot”—not just how you get the package to the customer’s door but into their house. At one point we tried to make an electronic lock with an electronic key that Amazon deliveries could use. But then someone else made Ring, and we realized we could use that instead.


Prompt: Amazon has aggressively marketed Ring to police, partnering with hundreds of law enforcement agencies across the country and in some cases even giving the devices away for free. Cops are given access to a portal they can use to request Ring footage from individual houses. Has Ring brought Amazon into much closer relationships with law enforcement? Relationships with law enforcement take a very long time to build, so it would really surprise me if any of those relationships were the result of the Ring acquisition. The groundwork was already being laid. I think Ring just helped accelerate things.

Response: In general, the nice thing about working with law enforcement is that they know what they want. Regulators don’t. But honestly, I think Amazon also kind of backed into that situation. We only realized after the fact that we had all this data about who was coming to people’s front doors. And then there was a lot of gleeful Mr. Burns–style finger touching, when we thought about what we could do with that data. Ring has a Neighbors app, where you could take your Ring data and share it with the app, and your neighbors could see who was in the neighborhood. So law enforcement was a natural next step. And law enforcement dovetailed nicely with our interest in pursuing the home security angle through our other smart home products.  Like the Alexa-enabled smart speakers.


Prompt: Right. Ring dovetails nicely with Alexa on the home security front. And Alexa could also help the retail effort to unlock the “last foot” and get packages inside the home. You said that Amazon was already building relationships with law enforcement long before the Ring acquisition. Why? AWS works extensively with US government agencies. At the federal level, AWS runs a special cloud for a number of intelligence agencies, and we’re still trying to get the contract for JEDI, the Pentagon’s big cloud project.

Response: The other factor that presumably contributes to Amazon’s coziness with intelligence agencies and law enforcement is the fact that many people from those fields go work for Amazon. Why is that? Amazon pays a better salary and you get to work on more exciting stuff, and there’s just less hassle.  Do the cultures mesh well? In general, the military is pretty top-down, command-and-control. That’s not Amazon. We mostly want people to be as autonomous as possible. At Amazon it's easy to shoot up the hierarchy and talk to senior executives if there's a problem.  Does Amazon make a concerted effort to hire from law enforcement and the military?  For the rank and file, yes. There is a concerted effort to recruit former law enforcement and military. In fact, Amazon thinks of military personnel as a diversity category and does targeted hiring. We have an internal affinity group called Warriors@Amazon for ex-military personnel, and it is by far our most successful diversity hiring group.  At the higher levels, there’s a revolving door. If you’re a chief procurement officer at the Pentagon, the guy who orders whatever they’re buying for the military, you do that for awhile and then you go to the General Accountability Office to be a watchdog. And then after you've been a watchdog for awhile, you go to work for Amazon, where you can make half a million dollars a year selling Amazon services back to the Pentagon. And you can get it past the watchdog because you used to be the watchdog.


Prompt: Amazon’s not the only company that does this, obviously. Everybody does. It’s the same twenty thousand people in the United States who have had jobs in the military, regulatory, and industry, all selling these things to each other back and forth. In September 2020, Amazon appointed Keith Alexander, the former NSA chief, to be on your board of directors. How did you and your coworkers see that?  At the most obvious level, it’s a move to get influence over the Defense Department and win more US government contracts. Because Keith Alexander has relationships with all of the right people in the Pentagon, he can help move the needle in terms of not only managing relationships with current officials, but also helping us strategize about what's important to the decision makers going forward.  But it was also highly controversial. Even within Warriors@Amazon, they’re of two minds about it. Some of the Warriors love the US government. On the other hand, bringing on someone like Keith Alexander also poses serious risks to Amazon's business.  How so? Our European partners are screaming about the message that hiring Keith Alexander sends with regards to the privacy of their data. It also comes at a particularly bad time. AWS needs the European market. But there have been a couple of recent legal decisions in Europe that have made our lives harder, like the courts striking down the Privacy Shield agreement in the summer of 2020. [Eds.: Privacy Shield was a compliance framework that certified the data security of US-based companies, and allowed them to receive EU user data in a manner deemed compliant with GDPR.] AWS has been working really hard with customers to make sure that we can comply with whatever new privacy standards the European Union develops.  So within this context, bringing Keith Alexander onto the board is definitely going to raise a lot of eyebrows on the other side of the pond. Our European partners are going to say, “Well, excuse me. Our concern is that you're taking data from our citizens and bringing it back to the States. And now you're hiring the architect of the program that’s been spying on the entire world?!” It sends mixed signals at best. But the fact that there’s a business case against it has also helped the people inside the company who are opposed to it for philosophical or ideological reasons.

Response: Are there many of them? A lot of my coworkers are concerned about Keith Alexander’s presence. I would say a quarter of the people at Amazon would identify with digital freedoms organizations like the Electronic Frontier Foundation, and they are pissed off. I think there are a lot of AWS employees who would rather quit than turn data over to the NSA. Those people are all up in arms and they’re some of the smartest and most innovative people at the company. I don’t think Amazon counted on so much internal opposition and resistance.  Inside Voices White-collar workers at Amazon have organized internal campaigns against the sale of facial recognition software to law enforcement agencies, and the sale of cloud services to companies that enable ICE. Has Amazon’s relatively large contingent of former law enforcement and military personnel made the organizing environment for these campaigns more challenging? My experience with ex-military people is that they love America but they don't care for the government. So they're like, “Yeah, I worked for Uncle Sam, but I could give a shit.” So I don’t think the presence of those people necessarily makes organizing harder. I just think the collapse of the Left in general over the past several decades has made workers everywhere believe that resistance is futile. Maybe we're doing right in the world, maybe we're doing wrong—but if we’re doing wrong, there's really very little we can do about it except some things around the margins.  It seems that most of the organizing energy at Amazon among white-collar workers has been around climate issues. There is an ongoing campaign to push the company to dramatically reduce its carbon emissions that have involved open letters to Bezos. And in April 2020, the company fired two white-collar workers who had helped lead the climate organizing, and who had also criticized the company’s treatment of warehouse workers during the pandemic. What’s your perspective? I didn't sign any of those letters, because I'm not sure what good they accomplish either internally at Amazon or externally in the world.


Prompt: In general, campaigns at Amazon fall into three groups. The first is mainstream causes that are palatable to everyone, like donating winter coats and protecting animal rights. The second is environmentalism. It's easy to find people at Amazon who care about global warming. So there is some support for people who recognize the severity of the problem and are willing to organize internally at Amazon around it. But that organizing effort is still pretty small. It has an outsized voice in the media, because everybody’s fascinated by Amazon. But there aren’t a ton of people who are paying attention to it internally.

Response: The third category of campaigns is social justice activism, like the #TechWontBuildIt campaigns against working with ICE or law enforcement. There’s a spirited debate on a couple of internal mailing lists about that kind of activism. But again, it's a small number of Amazon employees. Why do you think there aren’t a lot of people participating in this conversation internally? Historically, Amazon has probably had some of the worst internal communications tools of any large company. It's very difficult to discover active conversations happening in the company. There is no central clearinghouse. There is no internal social media. We recently got Slack, but it hasn’t made much of a difference for employees interested in organizing.


Prompt: Do you think the lack of internal communications tools is intentional? I think this is one of those cases where you should not presume malice. Amazon doesn’t actively not want employees to talk to each other. They just don’t see how employees talking to each other benefits productivity, morale, or the bottom line. If it did, and that impact could be quantified in some way, we'd have it tomorrow. But as it is, Amazon gives employees the tools that it thinks will help them get the job done. And they don't see employee fraternization as relevant to the job.

Response: Google, by contrast, has very robust internal communication infrastructure. And that infrastructure played an important role in facilitating organizing at Google. (Although more recently, management has been limiting the kinds of conversations that can happen on internal platforms.) Do you think the absence of similar tools helps explain why Amazon has seen comparatively less organizing? Well, if you look at Google, you'll notice they're headquartered in Mountain View, in the heart of Silicon Valley. And if you're an employee at Google and you're good at your job and you want to leave your job tomorrow, there are fifty-three employers out there that are going to be ready to hire you.


Prompt: If you're working in Seattle for Amazon and you're good at your job and you want to leave your job tomorrow, you have far fewer opportunities. Where are you going to go, Microsoft? There's not nearly as much mobility. So I think a big part of the reason we have less organizing is that people are more afraid to jeopardize their jobs. If you want to stay in the Northwest, you keep your head down.

Response: Statistically it is also more likely that an Amazon employee will have a family than a Google employee. So that’s another factor that makes people more risk-averse. Why should they do something that would potentially jeopardize their job? Particularly when it has a low chance of success?  As you pointed out, one of the reasons that the organizing efforts within Amazon have received so much media attention is because the media is fascinated by Amazon. There have been a spate of stories looking critically at Amazon’s market power, partnerships with law enforcement, labor conditions in its warehouses, and so on. Amazon also has prominent critics in national politics like Bernie Sanders.  How are these kinds of criticisms perceived from the inside? How do people respond to that sort of thing?  I think your question kind of misses the forest for the trees. For most people at Amazon, glancing at the Apple News feed on their iPhone is about as much of the discourse as they consume. They don’t care about the news. It doesn't contribute anything to their life. There are colleagues I'm friends with who don't really know who ran for president. They figure it's all going to be the same anyway, so why bother.


Prompt: But by the same token, if they hear someone criticize Amazon, they’re not inclined to be super defensive. There aren’t a lot of intense loyalists. People at Amazon are mercenaries. The company doesn't have great benefits. Office life kind of sucks and it’s not that fun of a place to work. It’s a grind. People work there because it pays a little bit better than the competition and it looks good on a resume. They can go in, do their job, go home, spend time with their kids, watch sports. That’s the good life.

Response: Amazon has around a million employees worldwide. The majority work in shipping and logistics and delivery. There are maybe eighty thousand corporate employees. And I would estimate that fewer than two thousand of them have participated in discussions around organizing. Do you see any cause for hope? In general, the people who are going to organize are the people who need to organize because they are fighting for their lives and their subsistence. Those are the people on the logistics side of Amazon who work at the distribution centers. Those are the members of the industrial proletariat in China who are manufacturing the things that are shipped out on the retail side. Those are the humans in developing countries doing piecework on Mechanical Turk.


Prompt: If there is going to be change, that’s where it will come from. I think that if you’re looking at corporate employees within Amazon as a source of hope, that’s ludicrous. The notion that these companies are going to repair the damage they’re causing by having white-collar workers organize internally to me is crazy. But maybe that's cynical and nihilistic. Maybe I’m a bad man.

Response: On the night of March 18, 2018, Elaine Herzberg was walking her bicycle across a dark desert road in Tempe, Arizona. After crossing three lanes of a four-lane highway, a "self-driving" Volvo SUV, traveling at thirty-eight miles per hour, struck her. Thirty minutes later, she was dead. The SUV had been operated by Uber, part of a fleet of self-driving car experiments operating across the state. A report by the National Transportation and Safety Board determined that the car's sensors had detected an object in the road six seconds before the crash, but the software "did not include a consideration for jaywalking pedestrians." In the moments before the car hit Elaine, its AI software cycled through several potential identifiers for her—including “bicycle,” “vehicle,” and “other”—but, ultimately, was not able to recognize her as a pedestrian whose trajectory would be imminently in the collision path of the vehicle.  How did this happen? The particular kind of AI at work in autonomous vehicles is called machine learning. Machine learning enables computers to “learn” certain tasks by analyzing data and extracting patterns from it. In the case of self-driving cars, the main task that the computer must learn is how to see. More specifically, it must learn how to perceive and meaningfully describe the visual world in a manner comparable to humans. This is the field of computer vision, and it encompasses a wide range of controversial and consequential applications, from facial recognition to drone strike targeting.   Unlike in traditional software development, machine learning engineers do not write explicit rules that tell a computer exactly what to do. Rather, they enable a computer to “learn” what to do by discovering patterns in data. The information used for teaching computers is known as training data. Everything a machine learning model knows about the world comes from the data it is trained on. Say an engineer wants to build a system that predicts whether an image contains a cat or a dog. If their cat-detector model is trained only on cat images taken inside homes, the model will have a hard time recognizing cats in other contexts, such as in a yard. Machine learning engineers must constantly evaluate how well a computer has learned to perform a task, which will in turn help them tweak the code in order to make the computer learn better. In the case of computer vision, think of an optometrist evaluating how well you can see. Depending on what they find, you might get a new glasses prescription to help you see better.


Prompt: To evaluate a model, engineers expose it to another type of data known as testing data. For the cat-detector model, the testing data might consist of both cats and other animals. The model would then be evaluated based on how many of the cats it correctly identified in the dataset. Testing data is critical to understanding how a machine learning system will operate once deployed in the world. However, the evaluation is always limited by the content and structure of the testing data. For example, if there are no images of outdoor cats within the testing data, a cat-detector model might do a really good job of recognizing all the cats in the testing data, but still do poorly if deployed in the real world, where cats might be found in all sorts of contexts. Similarly, evaluating Uber’s self-driving AI on testing data that doesn’t contain very many jaywalking pedestrians will not provide an accurate estimate of how the system will perform in a real-world situation when it encounters one.   Finally, a benchmark dataset is used to judge how well a computer has learned to perform a task. Benchmarks are special sets of training and testing data that allow engineers to compare their machine learning methods against each other. They are measurement devices that provide an estimate of how well AI software will perform in a real-world setting. Most are circulated publicly, while others are proprietary. The AI software that steered the car that killed Elaine Herzberg was most likely evaluated on several internal benchmark datasets; Uber has named and published information on at least one. More broadly, benchmarks guide the course of AI development. They are used to establish the dominance of one approach over another, and ultimately influence which methods get utilized in industry settings.  The single most important benchmark in the field of computer vision, and perhaps AI as a whole, is ImageNet. Created in the late 2000s, ImageNet contains millions of pictures—of people, animals, and everyday objects—scraped from the web. The dataset was developed for a particular computer vision task known as “object recognition.” Given an image, the AI should tag it with labels, such as “cat” or “dog,” describing what it depicts.

Response: It is hard to overstate the impact that ImageNet has had on AI. ImageNet has inaugurated an entirely new era in AI, centered on the collection and processing of large quantities of data. It has also elevated the benchmark to a position of great influence. Benchmarks have become the way to evaluate the performance of an AI system, as well as the dominant mode of tracking progress in the field more generally. Those who have developed the best-performing methods on the ImageNet benchmark in particular have gone on to occupy prestigious positions in industry and academia. Meanwhile, the AI systems built atop of ImageNet are being used for purposes as varied as refugee settlement mapping and the identification of military targets—including the technology that powers Project Maven, the Pentagon’s algorithmic warfare initiative.  The assumption that lies at the root of ImageNet’s power is that benchmarks provide a reliable, objective metric of performance. This assumption is widely held within the industry: startup founders have described ImageNet as the “de-facto image dataset for new algorithms,” and most major machine learning software packages offer convenient methods for evaluating models against it. As the death of Elaine Herzberg makes clear, however, benchmarks can be misleading. Moreover, they can also be encoded with certain assumptions that cause AI systems to inflict serious harms and reinforce inequalities of race, gender, and class. Failures of facial recognition have led to the wrongful arrest of Black men in at least two separate instances, facial verification checks have locked out transgender Uber drivers, and decision-making systems used in the public sector have created a “digital poorhouse” for welfare recipients.


Prompt: Benchmarks are not neutral pieces of technology or simple measurement devices. Rather, they and the measures that accompany them are situated, constructed, and highly value-laden—the reality of which is frequently discounted or ignored in dominant AI narratives. Datasets have hidden and complicated histories. Uncovering these histories, and understanding the various choices and contingencies that shaped them, can help illuminate not only the very partial and particular ways that AI systems work, but also help identify the upstream origins of the harms they produce. What we need, in other words, is a genealogy of benchmark datasets.

Response: Chair Inherits from Seat  Teaching computers how to see was supposed to be easy. In 1966, the AI researcher Seymour Papert proposed a “summer project” for MIT undergraduates to "solve" computer vision. Needless to say, they didn’t succeed. By the time the computer scientist Fei-Fei Li entered the field in the early 2000s, researchers had acquired a much deeper appreciation for the complexity of computer vision problems. Yet progress remained slow. In the intervening decades, the basics had been worked out. But there was still far too much manual labor involved.


Prompt: At a high level, computer vision algorithms work by scanning an image, piece by piece, using a collection of pattern recognition modules. Each module is designed to recognize the presence or absence of a different pattern. Revisiting our cat-detector model, some of the modules might be sensitive to sharp edges or corners and might “light up” when coming across the pointy ears of a cat. Others might be sensitive to soft, round edges, and so might light up when coming across the floppy ears of a dog. These modules are then combined to provide an overall assessment of what is in the image. If enough pointy ear modules have lit up, the system will predict the presence of a cat.  When Li began working on computer vision, most of the pattern recognition modules had to be painstakingly handcrafted by individual researchers. For computer vision to be effective at scale, it would need to become more automated. Fortunately, three new developments had emerged by the mid-2000s that would make it possible for Li to find a way forward: a database called WordNet; the ability to perform image searches on the web; and the existence of crowdworking platforms. Li joined the Princeton computer science faculty in 2007. There, she encountered Christiane Fellbaum, a linguist working in the psychology department, who introduced her to a database called WordNet. WordNet, developed by cognitive psychologist George A. Miller in the 1980s, organizes all English adjectives, nouns, verbs and adverbs into a set of "cognitive synonyms... each expressing a different concept." Think of a dictionary, where words are assembled into a hierarchical, tree-like structure instead of alphabetically. “Chair” inherits from “seat,” which inherits from “furniture,” all the way up to “physical object,” and then to the root of all nouns, “entity.”  Fellbaum told Li that her team wanted to develop a visual analog to WordNet, where a single image was assigned to each of the words in the database, but had failed due to the scale of the task. The resulting dataset was to be called ImageNet. Inspired by the effort, in early 2007 Li took on the name of the project and made it her own. Senior faculty at Princeton discouraged her from doing so. The task would be too ambitious for a junior professor, they said. When she applied for federal funding to help finance the undertaking, her proposals were rejected, with commenters saying that the project’s only redeeming feature was that she was a woman.

Response: Nonetheless, Li forged ahead, convinced that ImageNet would change the world of computer vision research. She and her students began gathering images based on WordNet queries entered into multiple search engines. They also grabbed pictures from personal photo sharing sites like Flickr. Li would later describe how she wrote scripts to automate the collection process, using dynamic IP tricks to get around the anti-scraping safeguards put in place by various sites. Eventually, they had compiled a large number of images for each noun in WordNet.


Prompt: However, they still needed a way to verify that the images actually matched the word associated with them—that all of the images linked to “cat” really showed cats. Since the scraping was automated, manual review was required. This is where Amazon's crowdworking platform, Mechanical Turk (MTurk), came in. It was a "godsend," Li later recalled. MTurk had been launched just a couple of years before, in 2005. Her team used it to hire workers from around the world to manually review the millions of images for each WordNet noun and then verify the presence or absence of a target concept.  The ImageNet dataset would take two and a half years to build, its first version completed in 2009. When it was finished, it consisted of fourteen million images labeled with twenty thousand categories from WordNet, including everything from red foxes to Pembroke corgis, speed boats to spatulas, baseball players to scuba divers. At the time, it was the largest publicly available computer vision dataset, hosted on the ImageNet website for anyone to download.  Convolutional Cat Ears Although it took an immense amount of effort to create ImageNet, the initial uptake was slow. Li and her students presented a poster announcing its creation at a major computer vision conference. Tucked away in a corner of a conference center in Miami Beach, they even distributed logoed keychains and pens to advertise it. But beyond ImageNet’s limited popularity, there was a deeper issue. The problem that Li had hoped to solve with the creation of ImageNet—the fact that object recognition modules needed so much manual work to produce—still hadn’t been solved.  In an attempt to encourage wider adoption of the dataset, Li’s team decided to organize a competition. The ImageNet Large Scale Visual Recognition Challenge was officially launched in 2010. To enter the challenge, competitors would develop machine learning models using the benchmark training data, and submit their model’s predictions on a set of the benchmark testing data. The team whose model could detect objects in the images with the highest accuracy would be the winner.

Response: In 2012, computer scientist Alex Krizhevsky, along with his colleagues at the University of Toronto, won the ImageNet Challenge with AlexNet, a neural network–driven machine learning model that outperformed all other competitors by a previously unimaginable margin. After a long period during which neural networks were out of fashion in AI, AlexNet almost single-handedly put them back at the center of research into the field. Part of what enabled the return of neural networks was the much greater processing power of modern computers, which was needed to handle massive datasets.  AlexNet helped fulfill the potential of ImageNet and solve the problem that Li had identified when she first started out: that computer vision required too much manual labor. Krizhevsky and his colleagues didn’t rely on handcrafted modules for object recognition. Rather, using neural networks, AlexNet was able to “learn” what an object looked like completely from the data.  Neural networks work by stacking layers of artificial “neurons” on top of each other. Each layer alters the image slightly, like a camera lens filter. Some of the first layers of AlexNet’s neural network model, known as “convolutional,” allowed it to automatically encode information that used to be manually coded—like the pointy edges of a cat’s ears. There was no longer any need to enter such information by hand. With enough images of cats, the neural network would be able to learn which patterns were most predictive of the animal.  AlexNet’s success is often credited with sparking the resurgence of neural networks—under the new name of deep learning, which refers to multiple stacks of neural network layers—as the dominant machine learning paradigm. The 2012 paper associated with the model now has over seventy-two thousand citations on Google Scholar, an indication of its popularity in academic and industry circles alike. Deep learning techniques have achieved near-universal adoption not only within computer vision, but also within natural language processing—which works with human language—and a number of related subfields.


Prompt: The deep learning era has, in turn, placed data—more specifically, vast quantities of data—at the center of AI development. Because deep learning models become more accurate when trained on more data, tech companies are highly incentivized to gather as much data as possible. The amount of information available on the internet continues to grow. Users on Instagram share 8.9 million images a day alone. Meanwhile, a new cottage industry of data annotation work has sprung up to feed soaring demand for data labeling. The people who do this work are typically subcontractors or crowdworkers, like the MTurkers who helped create ImageNet, and represent a growing underclass of invisible tech workers.

Response: Algorithms of Oppression Why does the history of ImageNet matter? ImageNet has had an enormous influence on the field of modern AI, and on many of the AI systems that affect so many aspects of our lives. By understanding the particular circumstances of ImageNet’s creation, we can better understand these systems. We can also understand how the progress of AI moves in fits and starts, how its reliance on massive amounts of data is contingent and accidental, and how its present course was just one possible path among many.


Prompt: ImageNet was built on three technological pillars: WordNet, search engines, and crowdworking. The reliance on WordNet has proven to be particularly problematic. ImageNet recodes outmoded and prejudiced assumptions—many of them racist, sexist, homophobic, and transphobic—because those assumptions come directly from WordNet.  A good illustration of this comes from a website called ImageNet Roulette. Developed by AI researcher Kate Crawford and artist Trevor Paglen, ImageNet Roulette allows users to upload images of themselves. These images are then analyzed by a machine learning model, trained on a set of ImageNet data, which generates a description. When Guardian journalist Julia Carrie Wong uploaded a photo of herself, it labeled her with an ethnic slur, while New York Times video editor Jamal Jordan was consistently labeled as "Black, Black person, blackamoor, Negro, or Negroid,” no matter which image he uploaded.  To their credit, ImageNet’s creators quickly sanitized the dataset of such labels for its future users. But those categories still exist in multiple machine learning systems, due in part to the influence of ImageNet. AI researchers Vinay Uday Prabhu and Abeba Birhane recently demonstrated that the categories in the WordNet database persist in several widely cited public computer vision benchmarks, resulting in the takedown of a prominent benchmark by MIT called Tiny Images. And if they exist in these open datasets, then they are potentially replicated in many internal industry ones.

Response: WordNet is not the only issue with ImageNet, however. The data contained within ImageNet was gathered from internet search engines in the early 2000s. Such search engines, as the UCLA professor of information studies Safiya Umoja Noble has explained, encode racist and sexualized imagery for Black, Latina, and Asian women, and overrepresent imagery of white men in positions of power. These engines also portray a Western white male vision of the world, associating "beauty" with white women, "professor" or "ceo" with white men, and "unprofessional hairstyles" with Blackness. These assumptions filtered into ImageNet as the dataset was constructed.


Prompt: One common response from AI researchers to the oppressive aspects of ImageNet, and to the crisis of algorithmic injustice more generally, is that the problem lies with the data: if we get more or different data, then all these problems will inevitably go away. This was the response that Yann LeCun, one of the “godfathers” of deep learning and chief AI scientist at Facebook, provided when a machine learning model designed to depixelate faces ended up whitening them as well. Timnit Gebru, co-lead of Google’s Ethical AI team, struck back, underscoring how AI systems cause real harm and exacerbate racial inequality, and arguing that improving them must mean more than just focusing on better data collection. (Disclosure: two of us, Hanna and Denton, are members of Gebru’s team.) Furthermore, data collection efforts aimed at increasing the representation of marginalized groups within training data are often executed through exploitative or extractive mechanisms such as, for example, IBM’s attempt to “diversify” faces by scraping millions of images from Flickr without the consent of people in them. As Gebru explained during a tutorial at the Computer Vision and Pattern Recognition conference in June 2020, “Fairness is not just about datasets, and it’s not just about math. Fairness is about society as well, and as engineers, as scientists, we can’t really shy away from that fact.” A particularly pernicious consequence of focusing solely on data is that discussions of the “fairness” of AI systems become merely about having sufficient data. When failures are attributed to the underrepresentation of a marginalized population within a dataset, solutions are subsumed to a logic of accumulation; the underlying presumption being that larger and more diverse datasets will eventually morph into (mythical) unbiased datasets. According to this view, firms that already sit on massive caches of data and computing power—large tech companies and AI-centric startups—are the only ones that can make models more "fair."

Response: A Genealogy for the Many Exploring the history of ImageNet has implications not only for how we discuss the problems and failures of AI, but also for how we make critiques and formulate solutions to those issues. We need to develop genealogies of data to show that datasets are the product of myriad contingent assumptions, choices and decisions, and that could, in fact, be otherwise. Genealogy is an interpretive method of analysis, which we can apply to the historical conditions of dataset creation. Understanding these conditions illuminates the origins of certain problems, but it also opens up new paths of contestation by enabling us to imagine new standards, new methods for evaluating AI progress, and new approaches for developing ethical data practices in AI.  Instead of the narrow focus on "bias," we can start to ask deeper questions such as: How did particular datasets emerge? What agendas, values, decisions, and choices governed their production? Who collected the data and with what purpose? Are the people represented in the datasets aware that they are participants in them? Can they meaningfully opt out? How about the workers, like the Amazon MTurkers, who annotated them? Were they fully recognized for their labor and fairly remunerated? And, most importantly, does the creation of the datasets serve the interests of the many or only those of the few? In March 2019, the Unicode Consortium, which controls the publication of emojis worldwide, released an emoji of a Hindu temple. Until then, a Hindu temple had been conspicuously missing from the set of emojis representing religious places, which included a Christian church ⛪ and Shinto shrine ⛩ (approved as part of Unicode 5.2, in 2009) and a kaaba 🕋, mosque 🕌, and synagogue 🕍 (Unicode 8.0, 2015). The most popular emojis are the ones that convey emotion, such as the crying-while-laughing face 😂, but many emojis are also powerful ways to represent cultures and identities (a woman in a headscarf 🧕🏽, same-sex couples 👬, different skin tones 👋🏻👋🏼👋🏽👋🏾👋🏿). On the face of it, then, the publication of an emoji evoking the religion of 1.2 billion people seemed like an important act of inclusion.


Prompt: But the form that the temple emoji took, and the timing of its release, carried distressing political connotations. Its publication coincided with a period of intensifying demands for a Hindu temple to be built on a contested site in the northern Indian city of Ayodhya. The construction of this temple is one of the most fraught political projects in contemporary India, and a symbol of violent attempts by Hindu supremacists to create a Hindu-first nation at odds with the ideal of a secular Indian democracy that emerged after Indian independence, in 1947.

Response: In 1992, Hindu fundamentalists from all over India traveled to Ayodhya to demolish a sixteenth-century mosque called the Babri Masjid. In the following months, at least two thousand people, mainly Muslims, were killed in violence between Hindus and Muslims across India. Much of this violence was led or inspired by the Rashtriya Swayamsevak Sangh (RSS), a militant Hindu nationalist organization that boasts millions of members throughout the country. The RSS has a two-pronged mission: to narrow the extraordinarily broad set of beliefs and practices known as Hinduism into a single political form of the religion, and to make India—which is roughly 80 percent Hindu, 15 percent Muslim, and 5 percent Christian, Sikh, Buddhist, Jain, and others—a formally Hindu state.


Prompt: If the destruction of the Babri Masjid was one of the RSS’s greatest symbolic victories, its greatest political victory came in 2014, when Narendra Modi, a longtime officer of the group, was elected India’s prime minister. Since then, the country has taken a brazenly fundamentalist turn, with mob killings of Muslims being implicitly and explicitly encouraged by members of Modi’s political party, the Bharatiya Janata Party (BJP), which is effectively the political wing of the RSS.  The mission to build a Hindu temple in Ayodhya has also advanced. In 2017, an extremist member of the BJP took power in India’s largest state, Uttar Pradesh, where the contested religious site is located. He promised that under his leadership, no force could stop the construction of the temple. Then, in November 2019, the long-awaited verdict in a twenty-seven-year-old court case was decided by India’s highly politicized supreme court: the construction of the Hindu temple, known as the Ram Mandir, could go ahead.

Response: This was the charged political climate into which the Hindu temple emoji was released by the Unicode Consortium earlier that year. Remarkably, the emoji bears two of the main emblems of Hindu political-religious fundamentalism: the color saffron, and a distinctive red flag that looks exactly like the banner of the RSS. The emoji’s design is also similar to the plans for the Hindu temple that is to be built in Ayodhya, which have been in development since the early 1990s.  The Hindu temple emoji can now adorn almost any message on any social media site in the world, whether or not users understand its significance. Many people, in India and abroad, put the emoji in their screen names to signal their allegiance to a Hindu-first nation; some also use it when making calls for violence against Muslims. But even when the emoji is used in less explicitly political contexts, its effect is to uphold and normalize the RSS’s political version of Hinduism, its violent attacks on Muslims, and its Hindu-supremacist vision of India.  Because it has a fairly superficial theory of what emojis are and remains focused on a narrow set of criteria for approving new emojis, the Unicode Consortium failed to understand the cultural and political significance of the Hindu temple emoji. The story of the emoji’s development reveals that the process for approving new emojis—arguably the most popular lingua franca in history—privileges the economic concerns of large tech companies, and ultimately replicates the ways that these companies see, and fail to see, the world.


Prompt: Hello, Interoperator? When you type a message into your smartphone or scroll through the articles on your Facebook feed, you are interacting with the work of the Unicode Consortium. The consortium is responsible for encoding all the characters you see on your screen—letters and numbers in various scripts, hanzi and kanji, dingbats—into binary, so that they can be read on pretty much any machine, with any operating system, anywhere in the world. The purpose is to ensure that a line of code written in Bangalore, or a tweet fired off from San Francisco, arrives at its destinations essentially unchanged. Want a functioning Bengali website that can be accessed just as easily in East London as in Dhaka, or a Chinese-manufactured tablet that can render fonts designed in Accra? You need Unicode for that.

Response: By encoding many of the world’s scripts—Latin, Arabic, Greek, Tamil—in this way, the Unicode Consortium plays a crucial role in determining who can use the internet, which languages will survive digitization, and who can reap the gains of the digital age. It also helps to determine who can enter the global digital marketplace as consumers, advertising targets, and data sources for extractive surveillance capitalism. In other words, universal standards for interoperability are not just about bringing people online and connecting them—they’re also about driving profits through an ever-expanding digital ecosystem.