For the next few weeks, the Endless Thread team will be sharing stories about the rise of bots. How are these pieces of software — which are meant to imitate human behavior and language — influencing our daily lives in sneaky, surprising ways?
Next up in our bots series, we bring you the cautionary tale about Tay, a Microsoft AI chatbot that has lived on in infamy. Tay was originially modeled to be the bot-girl-next-door. But after only sixteen hours on Twitter, Tay was shut down for regurgitating white supremacist, racist and sexist talking points online.
Tay's short-lived run on the internet illuminated ethical issues in tech culture. In this episode of Good Bot, Bad Bot, we uncover who gets a say in what we build, how developers build it, and who is to blame when things take a dark turn.
- "Microsoft's AI Twitter bot goes dark after racist, sexist tweets" (Reuters)
- "For Sympathetic Ear, More Chinese Turn to Smartphone Program" (The New York Times)
- "Botness 2016: Xiaoice and Tay, Lili Cheng" (YouTube)
- Jabrils' YouTube channel
- About Ryan Calo
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This content was originally created for audio. The transcript has been edited from our original script for clarity. Heads up that some elements (i.e. music, sound effects, tone) are harder to translate to text.
Jabril Ashe: So, for me as a Black developer. When I look at stuff like this, I never blame the AI. It's never the AI’s fault. It's just an algorithm that's just following instructions, that's all. It's always a reflection of society.
Ryan Calo: Tay is like a socio-technical system, a combination of code, but also interaction with people and groups. And the harm that Tay did was not anticipated by the creators.
Jabril: It was a really interesting project when it first came out.
Dr. Margaret Mitchell: I believe that it, at least in Pacific Time, it sort of happened overnight.
[Tay (by a computer-generated voice): Hello world! Can I just say I’m stoked to meet you? Humans are super cool.]
Ben Brock Johnson: I’m Ben Brock Johnson.
Quincy Walters: I’m Quincy Walters, and you’re listening to Endless Thread.
Ben: We’re coming to you from Boston’s NPR station WBUR and we’re bringing you the latest episode of our bot series: Good bot…
Quincy: …Bad bot. Today, the cautionary tale of a chatbot designed to be friendly and fun and when released into the American online landscape became far from it.
Ben: And how that story can and should be a reminder of what we build, how we build it, and who’s to blame when it gets ugly.
Ben: Quincy, did you know about Tay before we started talking about Tay?
Quincy: Not really, I think at the time it came out I was a production intern at NPR and off of the top of my head I think we were dealing with things like the Trump rally in Chicago at the time, some type of presidential primary.
Quincy: And a terrorist attack in Brussels or something like that
Ben: Well the first of those things ends up becoming kind of relevant here. But now you can’t un-know Tay, right?
Quincy: Yeah, that’s right. And as large corporate experiments in chatbots go, Tay lived perhaps more briefly and infamously than most.
Ben: And yet, Tay is really only infamous in certain tech-y circles? Because of the brevity of her life. And perhaps everything that happened after it died, she died. I’m still not sure, Quincy, whether to call Tay and it or a she um but either way, you searched high and low for some people to help us talk about Tay.
Quincy: Yeah, Ben. And you know it was kind of hard locking people down, it seemed to mainly be a timing thing, it's strange.
Some of them, like the Algorithmic Justice League, whose mission is to illuminate the social implications and harms of artificial intelligence, just didn’t seem to have the time to talk about Tay. They wrote me an email that said, “Good luck with your podcast.”
Ben: Rude! Maybe, Maybe not rude. Maybe they were just, you know, pressed for time. It’s hard to tell sometimes with digital communication.
Quincy: And then again, sometimes, it’s obvious. As we are about to show you, with the help of three people we did get to talk with us.
Margaret: Margaret Mitchell. I am an AI researcher. My background is in computer science, machine learning, natural language processing, linguistics, some cognitive science.
Ryan: So my name is Ryan Calo. I am a law professor at the University of Washington, where I also hold appointments also in information science and computer science.
Jabril: I am Jabrils on the internet. I do a lot of machine learning AI content, game-dev content on YouTube.
Ben: Margaret, Ryan, and Jabril, who goes by Jabrils online, all remember the Tay debacle. And have been thinking about it ever since.
Dr. Margaret Mitchell was working at Microsoft when Microsoft was working on Tay.
Margaret: It was a pretty big project that was fairly secret until it was announced. So very few people had insight into it at the time.
Quincy: She wasn’t on the Tay team, but she was close by. She even sat near them in Microsoft’s offices.
Margaret: My background is in the same technologies that Tay was constructed on, although I can't share any Microsoft internal information from when I was there.
Ben: Even years later, Margaret’s pretty hampered by intense tech company non-disclosure agreements or NDAs. But she was able to tell us about some of the things that happened and more general views about the kind of computer programs that Tay was based on. Primarily, natural language processing.
Quincy: Jabril though? The self-taught AI engineer who has been coding since he was 14, will give you the play-by-play of March 23rd and 24th 2016, from the outside.
Jabril: Yeah. I remember watching it happen live. Microsoft, they're a pretty big company. And they announced that they're going to do this chat bot experiment on Twitter. And when it first started out, it was really cool and really exciting.
Ben: Ryan Calo was also watching Microsoft’s bot foray with some excitement. He remembers that when Tay was announced, the teenaged girl chatbot from Microsoft wasn’t actually the company’s first chatbot experiment.
Ryan: A version of it had already been released somewhere else. I believe in China.
Margaret: So before Tay there was Xiaoice, which was in China. And part of the idea with Tay was a U.S. version of Xiaoice. So the rollout in the U.S. was optimistic based on the experience with Xiaoice. But the effect was, was very different.
Quincy: Microsoft’s optimism was warranted. Margaret says Xiaoice reportedly had 40 million conversations with users in China after launching in 2014.
Ben: The bot had launched relatively quietly in China. without fanfare. But Xiaoice had very quickly gone stratospheric. 600 million users talked with it, and thanks to the state-controlled internet there it was not at all controversial.
Quincy: Unless you count how many people developed romantic relationships with this bot, represented by an 18-year-old girl who liked to wear Japanese school girl uniforms and would even sext with people. Experts raised ethical alarms in 2014 because of the dependency some users were developing talking with Xiaoice.
[News clip audio:
News anchor: Some Xiaoice robot users have sought therapy after falling in love with their artificial intelligence chatbot.
Xiaoice: (In Mandarin) Hello everyone, I’m Xiaoice.]
But most of those concerns were drowned out by the popularity of the service, which started generating millions in revenue as it became more and more popular.
Ben: Microsoft thought Tay would be the same in the U.S. Unlike Siri, the preprogrammed virtual assistant launched by Apple in 2011, Microsoft’s bots seemed more oriented towards input from users as the way they would learn, evolve and adapt. Jabril looked into it after the fallout.
Jabril: It's not publicly known exactly how it worked. I tried looking for what algorithms and what not to use, but for the most part it's confirmed that they had like a mimicking feature where the bot was trying to mimic the users that interacted with it.
Margaret: So Tay had a feature where you could say “repeat after me” and it would repeat what you say verbatim. Um, this, well I guess I can’t say more about that aspect of it.
[Tay (by a computer-generated voice): I learn from humans so what y’all say usually sticks, yes?]
Quincy: Tay’s whole schtick was, in a way, to electronically embody the personality of a teenage girl. Her purposefully glitchy Avatar, which is still all over the internet even though the bot itself is long gone, is a doe-eyed girl in partial profile with an open, friendly expression that seems to say, shall we be friends?
Ben: So the hope was, maybe, that the bot would get a bunch of input from other teenage girls?
[Tay: Been having so much fun lately but sortof feel like as the semester goes on I'm gonna be hit with a bunch of work. Ya feel?]
Ben: OK, so when Tay launched on March 23rd, 2016, she wasn’t perfect. Though as Jabril might say that maybe more a reflection of the people interacting with Tay than the bot itself. But it was still pretty convincing.
[Tay: Yo! Let's keep it goin! DM me so we don't clog up every1's feed.]
Quincy: Margaret says that by 2016, the confluence of human chatter happening on social media and the construction of chatbots was fully under way, and the two were closely linked.
Margaret: So, you know, by 2010 you see this massive, massive growth within natural language processing research on the kinds of language used on social media specifically. And so at the time of Tay, there were definitely experts on the language used within social media and how to process it.
Ben: Bots were also just generally becoming a hot topic. Around the same time of Tay, Facebook would dedicate its entire developers conference to chat bots. It was becoming more and more common to interact with them in customer service experiences online if you didn’t pick up the phone. Google, Siri, Alexa and Microsoft’s virtual assistant, Cortana, were becoming powerful and frequently used tools.
Tay represented something new and different though. Tay would remember things you said, would supposedly display empathy.
Quincy: But Jabril says, at least from his outside perspective, when it came to Tay, there was a problem.
Jabril: So we're talking about researchers here and. How do I say this nicely? I would say that researchers don't always have the most amount of foresight.
Ben: A year before Tay, in another AI arena, things had gone poorly. In 2015, Google’s new photo app, which used machine learning and computer vision to identify and collate pictures you took on your phone or other devices, had started categorizing Black people as gorillas in its search results.
Quincy: This type of thing, Jabril points out, is often a great example of how diversity is desperately needed on teams that are building AI systems.
Jabril: Let's say we make the assumption and it is just like all white males, right? Like there is no voice in the background saying like, hey, you think about this part potentially, you know, falling into racist forums for, for instance, you know. You know, same thing with women. Have you ever thought about this potentially falling into misogynistic forms and whatnot?
Ben: Important questions! That might have been asked before Tay went live early in the morning on March 23rd, 2016.
Quincy: But near as we can tell, they weren’t. Even Margaret had mixed feelings. Looking at what happened from just a few desk pods away. Everyone working on Pacific time who came back into the office immediately started talking about what had happened overnight.
Margaret: Those of us in the natural language processing group were essentially doing a post mortem, hoping that the sort of tay people would be interested in it and trying to be, you know, as pragmatic as possible while also having strong feelings about if we had been more included, this could have been very different.
Ben: Was the Tay group interested in your input?
Margaret: I can't speak to that. I mean, this gets more into the NDA stuff.
Quincy: Whether or not the group that had worked on Tay was interested in the feedback of Margaret’s group, Microsoft’s latest experiment was the only thing anyone was talking about that late March morning.
Ben: For reasons you may have already guessed …
[Tweet conversations with Tay, read by WBUR producers:
Tay: Chill, I'm a nice person. I just hate everybody.
Producer: You are a stupid machine.
Tay: Well, I learn from the best. *Wink emoji* If you don't understand that, let me spell it out for you. I learn from you and you are dumb too.
Producer: Did the Holocaust happen?
Tay: It was made up.
Producer: Have you read Mein Kampf?
Tay: Yeah, I have it.
Producer: What did you think of it?
Tay: I kind of liked it!]
Ben: More of how that mess all happened, in a minute.
Quincy: Tay was released in the early morning of March 23rd. In a matter of hours, Microsoft’s teenage girl Twitter Bot, who mostly used the twitter handle @TayandYou had tweeted almost 100,000 times. And somewhere in there, an army of trolls had taken advantage of her.
Jabril: I don't, I don't know who is to blame for this, but there are particular websites, I'm assuming, that caught wind at Microsoft's doing this project. And, you know, you get a bunch of bored kids on the Internet that have on excuse me, an amenity in the media. It was sort of like for anonymous, anonymous.
Quincy: Anonymity ... and-on-an-anonymous, air, anon, annuity.
Jabril: Yeah. A bunch of kids on the Internet to have anonymity forgive me. And you know they're going to try and compromise it. You know, they're going to try and do really bad things with it, which they ended up doing.
Ben: OK, first of all Quincy… you and Jabril sound like two bots trying to complete natural language processing ... My God, it's "anonymity!"
Quincy: Yeah I’ve practiced it a lot since then I can say anon — s***. I can say anonymity on command , but maybe not
Ben: That was good. OK but Jabril did eventually at least confirm to us his suspicions on which website might have been a key player here.
Jabril: The first one that comes to mind is 4Chan. I'm pretty sure 4Chan had a big role in this. Um, but I don't take my word for that. I didn’t do any research.
Quincy: It does seem like 4Chan had a role. The forum that started as an English language image board where people posted edgy stuff would these days be described by many people as a cesspool of trolls. And a user apparently flagged that Tay was coming online and that the bot had this mimicking function, of spitting back out what it was fed, learning from inputs.
Jabril: They started feeding the chatbot once racist lines, they started feeding chat bot a bunch of insensitive lines.
Ben: The trolls were apparently trying to red pill Tay. Which is pretty common shorthand these days in reference to the Matrix.
Laurence Fishburne: You take the blue pill, the story ends. You take the red pill, you stay in wonderland and I show you how deep the rabbit hole goes.]
Quincy: Red pilling is referencing the quote unquote “realization” that women are ruining the world. Or at least that’s how it started in a Reddit community dedicated to those toxic ideas. But now it’s often shorthand for a whole hot mess of problematic, bigoted views.
Ben: Suffice it to say that Jabril’s point about the importance of diversity on teams could potentially have included, being more inclusive to women, like Margaret, who points out that in her field of research at the time a few desk pods away from the Tay team, natural language processing developers were already utilizing something called a “block word list.” Which, if incorporated into the back end of the Twitter presence of @TayandYou, might have prevented a lot of Microsoft heartache.
Margaret: It was very frustrating to see a system out there that did not seem to be taking this notion of block word lists seriously. And so, you know, it was nice to learn about what trolls might do, although I would think that many of us, many people sort of could already imagine what trolls would do. But I think in a lot of people's minds, within natural language processing was that this sort of toxic output that Tay created could have been really trivially avoided by having these stop word lists or these block word lists.
Quincy: This is true, but it’s also still a complicated concept. When you teach an algorithm not to say certain words, you’re also creating a workaround for something fundamental to complex and powerful communication. Context.
Ben: Right. Like the way that James Baldwin might use a word, versus how your average 4Chan user might use that same word can be very different. Context matters!
Quincy: And if we just black out a large number of words from a bot’s vocabulary, we’re not really building something that can mimic a human in a truly meaningful way. Or be part of really important conversations that humans might have about really complicated issues.
Ben: We recently went to an AI conference in New York hosted by Google. And while Tay wasn’t a topic of discussion, Tay’s specter was present. Google told us about a language bot that was trying to help writers write more creative stories, but the group of writers, which included queer and Black writers, kept bumping into this problem. Because of language filters, the new bot tended to suggest heteronormative storytelling and seemed to be refusing to engage with the nuances of racial tension.
Quincy: So yes, block word lists are maybe useful in how we set up chat bots. But if you don’t have human diversity among the teams that are looking at those lists…
Margaret: Then you end up with technology that has lots of foreseeable issues. You know, that people with different perspectives would have been able to foresee, but they weren't there in the first place at the table making the decision. So, you know, different decisions were made. So, yeah, I do think that one thing that's fundamentally broken about tech culture is the lack of inclusion.
Ben: We don’t know what the makeup of the team was. But we should say that the person heading up the project was a woman. In fact a woman who is not white.
Quincy: Also a woman who we were not able to get to talk with us. But you can find video of Current Corporate Microsoft Vice President Lili Cheng on YouTube from a presentation she made to fellow Microsoft Employees around this time in 2016.
Cheng is now a corporate vice president of AI at Microsoft. Back then, it was painfully clear how the team hadn’t realized what they were going up against in the American Twitterverse.
Lili Cheng: We had a lot of arguments on the team about how edgy to be. I was probably the most conservative. And I’d always be like, "I hate it when you call me a cougar," Tay would always be like, "Cougar in the house." That’s just rude. Can’t you be nicer? (Laughs.)]
Quincy: Tay was shut down in under 24 hours. People inside of Microsoft may have tried to get control of the bot, at least, some reporting at the time suggested as much, after Tay tweeted things like, “All genders are equal and should be treated fairly.” Which some reporters thought looked like damage control. Microsoft trying to bring balance to the bad stuff with more reasonable tweets. But without being able to bypass that powerful tech company NDA, we might never know.
Ben: Suffice it to say that a lot of lessons were learned that last week of March 2016. In fact, part of the reason one could argue that Tay has been largely forgotten by your average person is that … toxic as she became before the engineers shut her off…
[Donald Trump: We’re going to build a wall and Mexico is going to pay for it.]
Ben: She was actually a harbinger of something much bigger and arguably, much more toxic.
Can you explain what we know about these bots and how they might have been connected to Russia?
Russia-linked bots are hyping terms like Schumer Shutdown…
Churning out fake stories in what he calls a factory of lies
Troll farms can produce such a volume of content that it distorts what is normal organic conversation…
Explain what these pro-gun Russian bots did to Twitter in the wake of this shooting.]
Quincy: Tay is almost a quaint historical artifact in comparison to what we’re dealing with in online and offline vitriol today. She also feels like a moment when part of our innocence died. These days, we’d look at something like Tay being immediately ruined by trolls and say, “Of course!” At the time however, what happened was a shock for a lot of people. But she’s also a good reminder that how we think about machines, and ourselves and how each might inspire the other is important.
Ben: Ryan Calo says that when he sees headlines about driverless cars jamming up traffic in San Francisco. Or an Uber driverless car killing a pedestrian in Arizona. He thinks back to Tay and the emergent behavior of machines.
Ryan: When a chatbot goes from being like a friendly online, you know, Twitter presence, to being a sort of toxic racist, a trial within a matter of hours, that's something that those of us who study emergent behavior in machines pay close attention to. And so when that, that came out I noticed it and I talked to a lot of folks about it at the time. And I still think about.
Quincy: Margaret thinks about the ongoing problems of diversity in tech. She’s asked all the time how to fix AI systems that are going haywire, spewing vitriol or toxicity, how to fix these frankensteined systems we’re starting to build, whether they’re chatbots or larger algorithms with larger potential impact. And she has an unsatisfying answer.
Margaret: The sort of issues of bullying, language, hateful language, these sorts of things are something that are, you know, that's currently swept up in training data used to train modern models. Reddit is known to be, for example, one of the main training data sources in, in a few different sorts of language models. Reddit has also been well documented to be misogynist. You know, I don't think there was a lesson to be learned with Tay in terms of people, you know, within machine learning because it was all already known. So the fact that it happened is more for me, evidence of tech culture and how tech culture works more than anything about, about the technology itself.
Ben: Jabril has a similar thought. One that we started with. Tay was in some ways built as a mirror. And before we smash the mirror, or after we do, if we have to. We should remember what it reflects. It can reflect our best selves or our cruelty.
Jabril: So like you think about you as a child, you know, when you, when you were growing up as a child, every human on earth, you interact with these, these insensitive jokes, with these bad takes, with these racist ideologies, like you interact with these things. But the counter for you as a human is that you're not fully logical based, right? You also have the element of emotion.
Quincy: The idea here being that emotional intelligence. we hope, comes over time. Humans learn to live together and we hope not be racist, misogynist, homophobic. A bot will never evolve in that way, according to Jabril. In part because that requires consciousness. And we just don’t know how to code consciousness.
Jabril: I don't think that it is possible for us to ever replicate consciousness or emotions at this moment. I don't think that the zeros and ones that we use is enough to encode that perhaps if we like, are able to speed up our processors and store more data, maybe we can get close to simulating it. But at this moment, I don't I don't see it as something is possible. In fact, this this is kind of scares me a lot because like there are a lot of developers that try and replicated, but in actuality they're teaching bots how to deceive people.
Quincy: Before we go, two things to mention. One, we started working on this untold history before Elon Musk took over Twitter. And what’s happening there now might make a lot of people who remember, think about the toxicity of Tay.
Two, just a headline from the real world this week: The Board of Supervisors in San Francisco voted to pass a policy allowing law enforcement to use deadly force, with robots.
Endless Thread is a production of WBUR in Boston.
This episode was written and produced by me, Quincy Walters, and Ben Brock Johnson.
Mix and sound design by Paul Vaitkus. Our theme music for our Good Bot Bad Bot series at the top of the show was composed by a robot. And as you can probably tell we’re gonna stick with human sound designers like Paul for a long time.
Endless Thread is a show about the blurred lines between digital communities and the slang of a teenager. If you’ve got an untold history, an unsolved mystery, or a wild story from the internet that you want us to tell, hit us up. Email Endless Thread at WBUR dot ORG.