Speaker 1: Justice looks like, how do we use these tools in
Speaker 1: a joyful, uplifting manner versus just being reactive to the
Speaker 1: next times.
Speaker 2: There are No Girls on the Internet. As a production
Speaker 2: of iHeartRadio and Unbossed Creative, I'm brigitat and this is
Speaker 2: there are No Girls on the Internet. This week, President
Speaker 2: Biden signed an executive order to create some safeguards around
Speaker 2: the use of AI. This comes after black women women
Speaker 2: like doctor Joy Blomwini, founder of the Algorithmic Justice League
Speaker 2: and author of the new book of Masking AI, have
Speaker 2: been speaking up about the ways that technology like AI
Speaker 2: has already harmed marginalized communities and what needs to be
Speaker 2: done to stop it now. That last part is key
Speaker 2: because even though her groundbreaking research has been critical to
Speaker 2: understanding technology harms, doctor Blomweni's vision the future of technology
Speaker 2: is optimistic, blending poetry and technology. She asks, what is
Speaker 2: our collective, just and joyful vision for the future.
Speaker 1: Hello, my name is doctor Joy Bloomwini. I'm the founder
Speaker 1: of the Algorithmic Justice League and the author of Unmasking AI.
Speaker 1: My pronouns are she and hers.
Speaker 2: So I've heard you call yourself a poet of code
Speaker 2: which is awesome. What do you mean by that?
Speaker 1: So I am the daughter of an artist and a scientist,
Speaker 1: So I do feel I've grown up with the arts
Speaker 1: and science literally together, and so when I use the
Speaker 1: term poet of Code, it's really to reflect those two
Speaker 1: sensibilities which inform my work. So there's a major part
Speaker 1: of it which is storytelling and humanizing what's going on
Speaker 1: with evocative audits, you know, and portrayals. And then there's
Speaker 1: another aspect of it that is getting into the analytical
Speaker 1: technical pieces of what it means to evaluate a machine
Speaker 1: learning system or other types of AI applications. So the
Speaker 1: Poet of Code is very much indicating my origins as
Speaker 1: the daughter of an artist and a scientist.
Speaker 2: Do you think that that the way that you approach
Speaker 2: the work has really helped bring more folks into it
Speaker 2: Because I've been interested and invested in conversations about tech
Speaker 2: for a very long time. I did not care about slash,
Speaker 2: maybe even fully understand the implications around bias and things
Speaker 2: like AI until you and so you had this way
Speaker 2: of really making it visible, really making it poetic, really
Speaker 2: making me understand what was at stake.
Speaker 1: Do you think that.
Speaker 2: Part of why that is why folks feel so drawn
Speaker 2: to your work is because you make it so poetic, So,
Speaker 2: you know, story based really help people understand like where
Speaker 2: they fit into it.
Speaker 1: I think there is that element. So as I was
Speaker 1: doing my research at MIT that involves publishing research papers,
Speaker 1: and as fun as those are, you know, that's a
Speaker 1: very small community that will likely read those types of papers.
Speaker 1: So I wanted to say, how do I go from
Speaker 1: the performance metrics of evaluating an AI system to something
Speaker 1: like performance art. Why does this even matter? How do
Speaker 1: we get to the heart of all of these numbers.
Speaker 1: So if we see bias in a system and we
Speaker 1: quantify it, that's only part of the story. The other
Speaker 1: part of the story is what does that mean for
Speaker 1: someone who could experience algorithmic discrimination, algorithmic rature, or exploitation.
Speaker 1: And that's where the storytelling has to come in and
Speaker 1: it did for me. When I was a student at MIT,
Speaker 1: I had an opportunity to do research that showed large
Speaker 1: skin type gaps and gender gaps with the accuracy of
Speaker 1: different gender classification systems. So these are AI systems that
Speaker 1: look at a photo of your face and try to
Speaker 1: guess your gender. Where could that go wrong? Well, so
Speaker 1: we decided to do a bit of an evaluation. And
Speaker 1: after we ran the numbers and we showed there were
Speaker 1: large gaps and biases documented, I wanted to show people
Speaker 1: why it mattered.
Speaker 2: It does matter to all of us, whether you spend
Speaker 2: a lot of time thinking about it or not. This
Speaker 2: kind of technology is becoming more and more commonplace, despite
Speaker 2: the fact that it doesn't work so well on women
Speaker 2: or people with darker complexions, setting us up to disproportionately
Speaker 2: experience harm from its use. In gender shades doctor Blomini's
Speaker 2: groundbreaking research, she was among the first to uncover the
Speaker 2: gender and racial biases that plague facial recognition technology. But ever,
Speaker 2: the poet Doctor blomini spoken poem ai ain't ti a woman?
Speaker 2: Really brings the problem to life? Or AI misgenders and
Speaker 2: misidentifies famous black women in history like Michelle Obama, who
Speaker 2: facial recognition recognizes as a young man wearing a two pay.
Speaker 1: And so from that gender shades research project came the
Speaker 1: art piece that is AI ain't I a woman? Michelle
Speaker 1: Obama unabashed and unafraid to wear her crown of history,
Speaker 1: yet her crown seems a mystery to systems unsure of
Speaker 1: her hair, a wig of a fond a too pay
Speaker 1: maybe not? Are there no words for our brains.
Speaker 3: And our logs?
Speaker 1: Where I show tech companies you've probably heard of failing
Speaker 1: on the iconic women of people like Oprah Winfrey, Serena Williams,
Speaker 1: Michelle Obama, historic figures like sojourn nor truth. Hence the
Speaker 1: title AI AIN'TI a woman? And I saw that when
Speaker 1: I shared that poem. It's a video poem in all
Speaker 1: kinds of spaces, right you, defense ministers, you know, kids
Speaker 1: in middle school. It touched people's humanity in a way
Speaker 1: that the research couldn't, And that for me was really
Speaker 1: an important moment because for a long time I felt
Speaker 1: that I couldn't bring my art into my research because
Speaker 1: it might not be taken as seriously or it might
Speaker 1: lessen its impact. And I found just the opposite. When
Speaker 1: you humanize what's going on, it extends the reach of
Speaker 1: the people who feel they have a place in the
Speaker 1: conversation about AI or even like, oh, this is how
Speaker 1: it could matter to me, not some abstract Oh there's
Speaker 1: discrimination or tech can be harmful.
Speaker 2: These harms aren't abstract or theoretical. They're very real. And
Speaker 2: they're already happening. We talked about Portia Woodrif on the
Speaker 2: podcast before. She was heavily pregnant when she was falsely
Speaker 2: arrested and held for hours and needed to be hospitalized
Speaker 2: after being falsely arrested when police facial recognition misidentified her
Speaker 2: as a suspect that a carjacking she had nothing to
Speaker 2: do with. And she's not the only one. Back in
Speaker 2: twenty twenty, Robert Williams, a black man, became the first
Speaker 2: documented case of a person being falsely arrested thanks to
Speaker 2: the use of faulty facial recognition technology. Robert was arrested
Speaker 2: in front of his daughters after facial recognition mismatched his
Speaker 2: driver's license photo to someone who stole watches from a
Speaker 2: Shanola store in Detroit, but Robert had nothing to do
Speaker 2: with it. Tools like turn it in that are used
Speaker 2: to detect students cheating by turning in AI generated assignments
Speaker 2: routinely falsely accused of students of plagiarizing. According to the markup,
Speaker 2: the technology is much more likely to generate a false
Speaker 2: positive for international students and students who are non native
Speaker 2: English speakers. A group of Stanford Computer scientists found that
Speaker 2: seven different AI detectors flagged writing by non native speed
Speaker 2: makers as AI generated sixty one percent of the time
Speaker 2: on about twenty percent of the papers. That incorrect assessment
Speaker 2: was unanimous. Meanwhile, those same detectors almost never made such
Speaker 2: mistakes when assessing the writing of native English speakers. Obviously,
Speaker 2: these kinds of accusations could throw vulnerable students academic careers
Speaker 2: into turmoil. The people like Porscha and the international students
Speaker 2: speak up when they've experienced harms because of faulty technology,
Speaker 2: So are the powers that be listening? Do their experiences
Speaker 2: matter as much as the companies trying to make money
Speaker 2: from rolling out this technology do.
Speaker 1: But an example like Porscha Woodruff falsely arrested due to
Speaker 1: AI powered facial recognition. She was eight months pregnant, sitting
Speaker 1: in a jail cell, having contractions for crime. Now she
Speaker 1: was being held for a crime she didn't commit. And
Speaker 1: so when you hear those stories, the stories of who
Speaker 1: I like to call the X coded, you start to
Speaker 1: pay attention, right or maybe it's your kids and they
Speaker 1: got flagged for cheating. Turns out they didn't actually cheat.
Speaker 1: English is their second language. But some chat GPT detection system,
Speaker 1: right is flagging them as cheating. And so I do
Speaker 1: think those stories are what helps people see that this
Speaker 1: is a conversation that requires their voice. And it's so
Speaker 1: easy to think it's like I have a PhD from MIT,
Speaker 1: but I was doing all this before. Right, You don't
Speaker 1: have to have this type of in depth technical background
Speaker 1: to have a voice and to have an important perspective,
Speaker 1: because if you know you're being hard, that's enough. Yeah.
Speaker 2: A big part of what we aim to do here
Speaker 2: is to help people understand that you might not be
Speaker 2: an engineer, you might not have a doctorate, but you
Speaker 2: are the expert of your experience, and you use this
Speaker 2: technology every day or it's being used on you, and
Speaker 2: so you innately have a perspective that is valuable and
Speaker 2: worth sharing and worth hearing and worth centering about how
Speaker 2: that technology has impacted you.
Speaker 3: Let's take a quick break.
Speaker 2: At er back. People who are traditionally marginalized, like women
Speaker 2: and black women, are made invisible by technology. Every single
Speaker 2: day we're faced with silencing, erasure, and hostility. So is
Speaker 2: that one of the reasons why the technology that these
Speaker 2: spaces build also can't really see us? Do you ever
Speaker 2: feel I mean this stay with me here. I think
Speaker 2: that there is like a general hostility toward marginalized people,
Speaker 2: like toward black women. And I think in technology and
Speaker 2: I sometimes feel that the technology that is being made
Speaker 2: in turn mirrors that same hostility, mirrors that same erasure.
Speaker 2: And so because these facial recognition is not being tested
Speaker 2: or trained out us, you know, diverse data sets or
Speaker 2: whatever it in turn erases us, do you feel that
Speaker 2: there's like a that is kind of because of this
Speaker 2: underlying hostility toward people who are traditionally marginalized in the space.
Speaker 1: I actually don't think it's a intentional underlying hostility, which
Speaker 1: makes it even more dangerous. So well intentioned people right
Speaker 1: collecting data, doing what they think is good science, good
Speaker 1: machine learning, can still create harmful systems. And this is
Speaker 1: what I learned after we did different audits, and I
Speaker 1: would go talk to the teams behind some of these systems, right.
Speaker 1: They are nice people, you know, try to send the
Speaker 1: kids to college, right. And it was actually interesting to
Speaker 1: me because as much as I was wanting to humanize
Speaker 1: the people who are excoded, who are harmed by AI systems,
Speaker 1: part of what I try to do in the book
Speaker 1: as well is also humanize the people who are creating
Speaker 1: the systems and where things go wrong. But to your
Speaker 1: greater point, it's not an intentional hostility. Sometimes it is
Speaker 1: a profound and harmful ignorance to not even think to
Speaker 1: ask certain questions or to test the system in particular ways.
Speaker 1: That's part of what the research was about. We asked
Speaker 1: what happens when we put an intersectional lens on the
Speaker 1: way in which we analyze the performance of AI systems,
Speaker 1: and just doing that right opened up new areas of
Speaker 1: conversations where before people would just look at the overall score,
Speaker 1: and that gave us a false sense of progress within
Speaker 1: the space. Because we were testing these systems on benchmark
Speaker 1: data sets to see how well they do, and then
Speaker 1: you would look at the benchmarks, some of the benchmarks
Speaker 1: would be over eighty percent, lighter skinned individuals over seventy percent,
Speaker 1: you know, people identified as men. And if that is
Speaker 1: your benchmark of success, you're already not going to see
Speaker 1: how you're failing. And so when we created a more
Speaker 1: inclusive data set, et cetera, it allowed us to see
Speaker 1: that the promises of potentially well intentioned people weren't even
Speaker 1: panning out But there's even more to that, because I
Speaker 1: think with some of these conversations it can seem like
Speaker 1: it's a very technical problem with a technical solution. Data
Speaker 1: didn't detect, you know, system didn't detect the face, make
Speaker 1: it more inclusive, whatever else it is. But the problem
Speaker 1: is accurate systems can be abuse. We've seen facial recognition
Speaker 1: systems deployed at protests right, which we know can lead
Speaker 1: to chilling effects if you know you're under surveillance for
Speaker 1: daring to exercise your First Amendment rights to say this
Speaker 1: is not correct. Accurate systems create tools for state surveillance.
Speaker 1: So yes, you can say, well, our my phone tracks
Speaker 1: me done. The other you can leave your phone at home.
Speaker 1: Your face is a little bit harder. You know, some
Speaker 1: people do put on a face, but you know what
Speaker 1: I mean. You know what I mean, right, So I
Speaker 1: think it's important to understand that even when we have
Speaker 1: conversations about the accuracy of certain systems, and we should
Speaker 1: have those conversations, accuracy is not enough to assure accountability
Speaker 1: or equity.
Speaker 2: Now, when we're talking about accountability, especially from tech companies,
Speaker 2: it is so easy to get caught in a cycle
Speaker 2: of name and shame where you point out all the
Speaker 2: bad things that a specific company has done. And if
Speaker 2: I'm being honest, I might have done that a time
Speaker 2: or two on this very podcast. But doctor Bloomwini describes
Speaker 2: their as less name and shame and more name and change.
Speaker 2: They want to show companies what they're doing wrong so
Speaker 2: they can change for the better. But this hasn't always
Speaker 2: meant that those companies don't lash out when her teams
Speaker 2: point out the harm that they've caused. I'm curious, how
Speaker 2: have companies I won't say any names, but companies who
Speaker 2: you have called out in your research or you know,
Speaker 2: said like this is hey, this is what's going on.
Speaker 2: How have they responded to your findings?
Speaker 1: So overall, I take a name and change approach. So
Speaker 1: the point of pointing out what's wrong isn't to shame
Speaker 1: a company, is to say we can do better. Right,
Speaker 1: And sometimes we have companies that are reactive. We have
Speaker 1: companies that are proactive, and we have companies that are combative.
Speaker 1: With the first set of research results that we released,
Speaker 1: we saw more of the reactive stance, which is is oh,
Speaker 1: now that there's a headline, right, we're gonna go. We
Speaker 1: are on the problem or we have or we were
Speaker 1: already working on the problem. You know, they're different ways,
Speaker 1: but now it's a priority because it's making headlines. So
Speaker 1: I saw that and the reactive approach tended to be
Speaker 1: a technical approach, which is okay, there were these disparities,
Speaker 1: so let's close them. We now have more accurate XYZ. Again,
Speaker 1: accurate systems can be of use. Then we did experience
Speaker 1: some combative responses. Right, so here we had a huge
Speaker 1: tech company coming out and saying your research is misleading,
Speaker 1: attempting to discredit the research of At that time, I
Speaker 1: was a graduate student, and I was so fortunate, you
Speaker 1: know that I had senior scholars and people well respected
Speaker 1: in the AI industry who came to our offense, you know,
Speaker 1: cheering prize winner, somebody who was literally the chief AI
Speaker 1: scientist at that company, saying, what the research shows warrants
Speaker 1: our attention, and this is research we should be elevating,
Speaker 1: not dismissing, because it makes the field better as a hold,
Speaker 1: if we can acknowledge our limitations, understand what's going wrong,
Speaker 1: so we can build more robust systems. Because this doesn't
Speaker 1: just deal with faces. Right, If you want to use
Speaker 1: computer vision to help. Let's say, with medical diagnoses, you
Speaker 1: want to make sure you understand where things can go
Speaker 1: wrong so we can course correct for things to go right.
Speaker 1: So we had the combative approach, the reactive approach, but
Speaker 1: the approach I appreciate most is the proactive. Bro Okay,
Speaker 1: we've heard there's some issues. Instead of waiting for someone
Speaker 1: to drop the paper or the headline, what can we
Speaker 1: be doing as a company now. And I've had the
Speaker 1: opportunity to work with Procter and Gamble with Olay on
Speaker 1: the Decode the Bias campaign. And when they came to
Speaker 1: me and they asked for an algorithmic audit, I said,
Speaker 1: given what you've described, your tool does and this was
Speaker 1: a tool that would analyze your skin and give you
Speaker 1: product recommendations. Ah, and how you train the tool on
Speaker 1: a set of data. I suspect if we dig in there,
Speaker 1: we're gonna find some bias. They're like, that's okay. If
Speaker 1: you find bias, we'll do what we can to correct it.
Speaker 1: I was like, and if you can't correct it, we'll
Speaker 1: shut the system down. Like can I get this in writing?
Speaker 1: I never I never hear this. I never hear this.
Speaker 1: And then my other question was if we did an audit,
Speaker 1: could we publish the audit results, because that adds another
Speaker 1: level of transparency, so it's not just up, we got checked,
Speaker 1: but no one knows what happened. Right. They agreed to
Speaker 1: all of those things we did and in fact find
Speaker 1: bias as we thought would be there, and they actually
Speaker 1: and the proactive Not only did they seek to be audited,
Speaker 1: they also agree to a consented data promise. And this
Speaker 1: was inspired by their skin promise. So when I first
Speaker 1: started working with LA I was excited. And then they
Speaker 1: told me, you know, when we do the campaigns, there
Speaker 1: won't be any post production air brushing. What we capture
Speaker 1: is what will show, right, you know, truth and advertising.
Speaker 1: You want to think about people's body image and all
Speaker 1: of that. And I'll be honest, I was a little
Speaker 1: disappointed because, like, if you you just want to know,
Speaker 1: you could be saved by the airbrush.
Speaker 3: Right.
Speaker 1: But because of that promise, which is a good promise,
Speaker 1: I get where they're coming from. As the person on
Speaker 1: the other side of the four K camera in your face,
Speaker 1: you're asking, can we consider XYZ? But what I appreciated
Speaker 1: about that is it made me even more disciplined with
Speaker 1: my actual skincare regimen, and I also drank water, and
Speaker 1: I did all the right things for vanity reasons. I
Speaker 1: won't I did the right things for vanity reasons, but
Speaker 1: I think about that with so that skincare that promise, right,
Speaker 1: that was part of the inspiration for the consented data promise.
Speaker 1: And just like when you make a promise and it's
Speaker 1: a public commitment, that's the important part. Now there's a
Speaker 1: little bit of accountability. Right. So now you are going
Speaker 1: to bed early. Now you are drinking more water. Now
Speaker 1: you are exercising five days of the week, which might
Speaker 1: not have been the case before you made that public.
Speaker 1: So that's an example of more proactive. So from the
Speaker 1: reactive to the combative to the proactive, we've seen it all.
Speaker 1: I think what I learned most from the bative response
Speaker 1: was how much of a risk I was taking as
Speaker 1: a young researcher to not only do their research, but
Speaker 1: to name the companies and I'll name them now, right,
Speaker 1: that I tested IBM, that I tested Microsoft, that I
Speaker 1: tested Amazon, and because of their power, that meant I
Speaker 1: was risking future opportunities and also other researchers watching how
Speaker 1: I was treated, how my co authors were treated people
Speaker 1: like doctor Gabru. They were also getting a sense of
Speaker 1: what is possible When I look at research papers now
Speaker 1: where people openly talk about algorithmic bias and algorithmic harms,
Speaker 1: and people openly name the AI models or the tech companies.
Speaker 1: That wasn't always the case.
Speaker 3: Right.
Speaker 1: A price was paid for this more robust conversation to happen.
Speaker 2: Doctor Blomini is right, this all comes out of cost.
Speaker 2: Doctor tim Net Gabrew, who she mentioned earlier, was a
Speaker 2: co author on doctor Blomini's gender shades research. Doctor Cabrew
Speaker 2: was once the technical co lead of the Ethical Artificial
Speaker 2: Intelligence team at Google. While in that role, she worked
Speaker 2: on a paper about the risks of large language models
Speaker 2: from environmental impact to bias. Google demanded she withdraw the paper.
Speaker 2: It got contentious, the conversation was hostile, and the whole
Speaker 2: thing was highly gendered and racialized. Doctor Cabrew was belittled, discredited,
Speaker 2: and harassed online, and it ended with her termination.
Speaker 1: It cost us something for doctor Gabru, you know, it
Speaker 1: cost her her job to speak up when she saw
Speaker 1: some of the issues that we see in what they
Speaker 1: call large language models, the type of AI systems that
Speaker 1: will power chat GPT right, and so I do think
Speaker 1: the timing of the different types of company responses also
Speaker 1: made a difference in my own trajectory. The first response
Speaker 1: I had when the gender Shades paper was published was
Speaker 1: IBM invited me to their headquarters. You know, I spoke
Speaker 1: to their team members. They actually had released a new
Speaker 1: model by the time I was presenting that research, and
Speaker 1: I could share what their results had been. And then
Speaker 1: later we did our own study. So that was a
Speaker 1: very different reception. That reception gave me home. I was like, Okay,
Speaker 1: all right, let's work together. Amazon situation. I don't know,
Speaker 1: I don't know how corporate anymore sort of thing. But
Speaker 1: I'm being you know, I'm putting these as more extreme cases.
Speaker 1: But the point being, we can't really just wait on
Speaker 1: if a company's going to choose to be reactive, proactive,
Speaker 1: or combative. What we really need are laws and regulations
Speaker 1: that don't rely on the goodwill of companies. Yeah.
Speaker 2: I mean, I have to ask, when you were this
Speaker 2: young researcher naming these companies in your in your findings,
Speaker 2: did you know that you were taking on such a
Speaker 2: personal risk or were you like, oh wait, glad it
Speaker 2: worked out. Glad people had my back. Did you know
Speaker 2: that that was a risk that you were encourring and
Speaker 2: did it anyway or did you sort of do you
Speaker 2: sort of look back and think like, Wow, I'm really
Speaker 2: glad that worked out.
Speaker 1: I knew that once the research was published it would
Speaker 1: be questioned, so before it was published, I actually sat
Speaker 1: with a law clinic.
Speaker 3: Right.
Speaker 1: We went through what could be said, right, what might
Speaker 1: actually put you in legal jeopardy, and so forth. So
Speaker 1: I didn't go into the situation not thinking there might
Speaker 1: be blowback. I was actually surprised with the first round
Speaker 1: we prepared and they're like, oh, yeah, okay, these are issues.
Speaker 1: Come to the headquarters XYZ, we released new models, et cetera.
Speaker 1: The blowback that I got with the second paper is
Speaker 1: what I had thought I might experience, but just the
Speaker 1: magnitude of it I wasn't ready for. I remember what
Speaker 1: the film Coded Bias available on Netflix. It shares part
Speaker 1: of the story of graduate students Darnalgorithmic Justice League, and
Speaker 1: examples of people experiencing real world AI harms. The people
Speaker 1: to provide the insurance for that film were nervous because
Speaker 1: we critiqued Amazon. It wasn't that Amazon had said anything.
Speaker 1: It was just a acknowledgment of Amazon's power, right, And
Speaker 1: so it didn't dawn on me just how powerful some
Speaker 1: of these tech companies are. I remember being at an
Speaker 1: international summit in Switzerland, and it was as if the
Speaker 1: heads of the tech companies were heads of state, you know,
Speaker 1: and so observing that closer made me really I was like, Oh,
Speaker 1: I'm like, okay, I'm poking. It's like, okay, I'm poking
Speaker 1: a dragon. I'm like, oh, it's a fire breathing dragon. Oh,
Speaker 1: it's like a dragon dragon.
Speaker 2: And like when you go to kill a bug and
Speaker 2: you're like, oh, it's got wings, it flies, right.
Speaker 1: So I knew, like, you know, it's not going to
Speaker 1: be the best situation. But I don't think I was
Speaker 1: fully prepared, though I thought I had prepared.
Speaker 3: More after a.
Speaker 2: Quick break, let's get right back into it. Even though
Speaker 2: Amazon tried publicly discrediting doctor Blomini's work calling out the
Speaker 2: harms of their facial recognition technology, in the end, they
Speaker 2: conceded that technology wasn't exactly safe. In twenty twenty, they
Speaker 2: announced a pause on allowing police to use the technology,
Speaker 2: and eventually extended that pause indefinitely and correct me if
Speaker 2: I'm wrong. But your work ended up with Amazon rolling
Speaker 2: back some of the uses of their faulty facial recognition technology.
Speaker 2: So ultimately, not only were you obviously vindicated, but that
Speaker 2: work went on to create a somewhat like safer landscape
Speaker 2: for everyone because Amazon had to step back and be like, Okay,
Speaker 2: wait a minute, this technology maybe isn't really working that well.
Speaker 1: They would not put it in those terms, but they
Speaker 1: did make they didn't take other steps. So I will
Speaker 1: say before Amazon, IBM actually said we are no longer
Speaker 1: going to sell to police departments. And this was in
Speaker 1: twenty twenty, right, so we also had the murder of
Speaker 1: George Floyd happening at that time, and Microsoft said we
Speaker 1: will not sell this until regulations are in place. And
Speaker 1: then Amazon came third, you know, and they said we'll
Speaker 1: halt it for a year, and then they extended that halt.
Speaker 3: Right.
Speaker 1: But this is to say there was an acknowledgment right
Speaker 1: of the risk and the harms. Was it just risk
Speaker 1: and harms to people, or risk and harms to the
Speaker 1: company's reputations. It could be a combination of both. I'm
Speaker 1: excited to share that this research led to numerous cities,
Speaker 1: you know, incorporating some of the findings in their analysis
Speaker 1: and in their statements for why they chose to enact
Speaker 1: certain laws that restrict police use of facial recognition. It
Speaker 1: also changed the conversation at the national and international level
Speaker 1: that EUAI Act actually has a provision that would prevent
Speaker 1: the use of life facial recognition in public spaces. When
Speaker 1: I spoke with President Biden at the AI Realm Table
Speaker 1: some time ago, this was top of mind. I shared
Speaker 1: the story of Robert Williams being wrongfly arrested in front
Speaker 1: of his two daughters. We talked about racial bias in
Speaker 1: AI systems and other types of harms that can impact
Speaker 1: many people because no one, trust me, no one is immute.
Speaker 1: This isn't just other people's problems. And so to see
Speaker 1: the reach of that work certainly made all of the
Speaker 1: combative you know, responses and things like that somehow worth it.
Speaker 2: And it really goes back to what you were saying
Speaker 2: earlier about how systems don't have to be biased to
Speaker 2: be misused, and like, don't know, I want to believe
Speaker 2: in a tech landscape where companies with so much power
Speaker 2: don't have to wait until something goes wrong, don't have
Speaker 2: to wait until somebody is wrongfully arrested, don't have to
Speaker 2: wait until they're called out to make things a little
Speaker 2: bit safer and more equitable, Like, do you believe that
Speaker 2: is possible where companies aren't just reacting, they're actually you know,
Speaker 2: being proactive at wanting the technology that they deploy on
Speaker 2: all of us to be more equitable.
Speaker 1: I think that again, you do see companies taking on
Speaker 1: the mantle of responsible AI. You'll have other companies like
Speaker 1: Credo dot Ai that will have services that are meant
Speaker 1: to help companies adopting AI systems do it within a
Speaker 1: responsible way. You'll see companies hiring responsible AI leads, right,
Speaker 1: So I definitely think there is an intention there. Where
Speaker 1: I still push back a bit is self regulation is
Speaker 1: always self interested, not surprising, So I do think real
Speaker 1: accountability requires external accountability. And the other part that I
Speaker 1: don't really see companies focused on so much is redress.
Speaker 1: So there's a lot of conversation about being responsible in
Speaker 1: terms of preventing future harms, but what about those who
Speaker 1: have been harmed already? And I do think algorithmic redress
Speaker 1: is oftentimes missing from this conversation of responsible AI. So
Speaker 1: when I see the company stepping out to say and
Speaker 1: we're doing redress, I might be convinced I haven't seen
Speaker 1: it yet, though, prove me wrong. Prove me wrong. I
Speaker 1: want to be wrong.
Speaker 2: Well as somebody at the helm of an organization fighting
Speaker 2: for algorithmic justice, what does justice look like?
Speaker 1: Justice looks like you live in a world where data
Speaker 1: is not destiny, where you're hue is in a cue
Speaker 1: to dismiss your humanity, where you actually have data rights
Speaker 1: and you can consent to how your information is used.
Speaker 1: Justice looks like how do we use these tools in
Speaker 1: a joyful, uplifting manner versus just being reactive to the
Speaker 1: next time. So when I think of social justice, you
Speaker 1: can't have social justice without algorithmic justice. Because if you're
Speaker 1: saying we're pushing for gender equality, yet you have an
Speaker 1: AI system that cuts out women's resumes, we didn't quite
Speaker 1: think it right. You can't necessarily say, oh, we have
Speaker 1: racial equality and then you're adopting bias facial recognition. That's
Speaker 1: putting so far. The folks I've seen have all been
Speaker 1: dark skinned like us, you know, into prison due to misidentification,
Speaker 1: And so me for me right, Algorithmic justice is truly
Speaker 1: being in that place where we can be our full
Speaker 1: selves and not be targeted right or algorithmically placed as
Speaker 1: other algorithmically erased, algorithmically exploited. And so that's the world
Speaker 1: we fight for. Right we say, free the X coded,
Speaker 1: and so this is algorithmic justice.
Speaker 2: The book is unmasking Ai your namesake, Joy. I have
Speaker 2: to tell you you are such a joyful person. Speaking
Speaker 2: to you about this work is it just comes through
Speaker 2: how much you care and how I don't know. I
Speaker 2: have a lot of conversations about tech that are hard
Speaker 2: and dark and grim, and your work is just so
Speaker 2: the opposite. It asks what if, what's possible? What can
Speaker 2: we do? How can things be better? Like it's just
Speaker 2: really nice to see somebody leading the way with such
Speaker 2: a joyful but justice rooted perspective. It's so refreshing.
Speaker 1: Thank you, and it's so wonderful to be in conversation
Speaker 1: with you. I love supporting Fi bad Ass. You said
Speaker 1: I could cuss women, so this is great.
Speaker 2: So how can folks learn more about the Algorithmic Justice League.
Speaker 1: I'm so glad you asked. We do have ww dot
Speaker 1: AJL dot org and so we invite people to be
Speaker 1: agents of change and join the Algorithmic Justice League. We
Speaker 1: have a library there as well, so if you're new
Speaker 1: to this area and you are curious you know what
Speaker 1: is even AI, We've created our resources for you so
Speaker 1: you can be part of the conversation. And we also
Speaker 1: have an ex Coded Experiences platform, So, like you were saying,
Speaker 1: you are the expert of your own lived experience, and
Speaker 1: we value that expertise, so we do campaigns where people
Speaker 1: can tell their stories of being ex coded. For some people,
Speaker 1: we just launched a campaign about facial recognition in airports,
Speaker 1: so people are sharing if they saw signage, if they
Speaker 1: knew they could opt out, and all of that actually
Speaker 1: builds a database of stories that shows if the TSA
Speaker 1: or others are actually doing what they say. They said
Speaker 1: it was optional. I didn't even know I could opt out.
Speaker 1: We have a disconnect, but we also have the data, right,
Speaker 1: So I do think people as they are encountering various
Speaker 1: AI systems and they have questions or stories to share,
Speaker 1: AJL is that place they can go to.
Speaker 2: So please check out AJL it is you're doing such
Speaker 2: incredible work. Thank you so much for being here, and
Speaker 2: just thank you for being you. Thank you for being
Speaker 2: in the space. We need more people like you.
Speaker 1: Thank you for having me.
Speaker 2: Black women like doctor Blowini have been speaking truth to
Speaker 2: power when it comes to AI, and it's critical that
Speaker 2: the people who hold that power are listening. Her new book,
Speaker 2: A Masking AI is poised to be one of the
Speaker 2: most important books about technology of the year, and it
Speaker 2: could not have come at a better time. Available now,
Speaker 2: so I'll hope that you'll join me in reading it.
Speaker 2: If you're looking for ways to support the show, check
Speaker 2: out our merch store at tangoty dot com slash store.
Speaker 2: Got a story about an interesting thing in tech, or
Speaker 2: just want to say hi, You can read us at
Speaker 2: Hello at tangodi dot com. You can also find transcripts
Speaker 2: for today's episode at tengody dot com. There Are No
Speaker 2: Girls on the Internet was created by me Bridget Tood.
Speaker 2: It's a production of iHeartRadio and Unbossed Creative edited by
Speaker 2: Joey pat Jonathan Strickland is our executive producer. Tari Harrison
Speaker 2: is our producer and sound engineer. Michael Almada is our
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