Latest / Paths of Curiosity in an AI World / Q2 - Power, Law, and Civic Life: When Algorithms Govern Your Life with Clarence Okoh, JD

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Q2 - Power, Law, and Civic Life: When Algorithms Govern Your Life with Clarence Okoh, JD · Paths of Curiosity in an AI World

• 0:04
  Welcome to Pass of Curiosity in an AI world where we explore Tamara's careers through the lens of AI.
• 0:14
  In a school district in Florida, seventeen thousand children were secretly placed on a predictive criminal list based on their grades in attendance.
• 0:25
  Clarence Oka is a civil rights attorney who investigated that program and helped shut it down.
• 0:32
  Let's get curious.
• 0:39
  Today's guest has spent his career fighting something most people don't know is happening.
• 0:45
  AI systems are quietly making life-altering decisions about black and brown communities in schools, courtrooms, and hiring offices without oversight or accountability.
• 0:58
  Clarence Oka is currently the senior attorney for civil rights and technology at Tectonic Justice, a new national digital rights organization addressing the impact of AI and algorithmic systems on low-income communities.
• 1:14
  He is a co-founder of the No Tech Criminalization and Education Notice Correlation, an inaugural member of the Just Tech Fellowship of the Social Science Research Council, and a formal civil rights litigator and equal justice works fellow at the NAACP Legal Defense Fund.
• 1:35
  Before that, he served as senior policy counsel at the Center for Law and Social Policy, not as a class, where he led cross-team work on the civil and human rights implications of emerging technologies for communities of color with low income.
• 1:52
  Alabama native and the son of a family whose history of racial injustice in the deep south is the direct foundation of his life's work.
• 2:01
  His advocacy has shaped federal, state, and legal policy.
• 2:05
  And his writing has appeared in Politico, Fast Company, The Nation, Vice, and many others.
• 2:11
  Mr.
• 2:11
  Oka, thank you so much for joining us.
• 2:14
  Thank you for having me.
• 2:16
  So let's start off with something straightforward.
• 2:18
  How would you describe your work to a student today?
• 2:21
  Yeah, so again, thank you all for having me for this podcast.
• 2:24
  Yeah, so I will describe my work as I'm a civil rights attorney and I work with communities that are experiencing injustices at the hands of artificial intelligence and other emerging technologies.
• 2:36
  So a lot of my work is about using the law as a tool to protect our communities against the harmful uses of artificial intelligence.
• 2:44
  You've said your personal family history is inseparable from the work you do today.
• 2:50
  How does that shape the way you approach a career in law and technology?
• 2:56
  So, by background, as you said, I'm originally from Alabama and have really devoted my life to fighting against a lot of the conditions that I saw growing up as a young black man in Alabama.
• 3:07
  Unfortunately, a lot of the promises of the civil rights era have not materialized in the day-to-day experiences of black and brown communities in the South and across the country.
• 3:17
  And it was that justice gap, right?
• 3:18
  That gap between the justice that we were promised and the realities that we experience that for me felt like a calling to service, to figure out ways of actually living out to the full promise of equality under law for all people.
• 3:31
  And to ensure that no group of people in this country experiences a type of injustice, inequality, oppression, and second-class citizenship that my grandparents and so many other generations have experienced in the South and beyond.
• 3:43
  Did you know from the start that this is what you wanted to do?
• 3:47
  So it's interesting.
• 3:48
  I say that because we recently just had the folks at NASA on Artemis II return home.
• 3:53
  And when I was a kid, I remember this in the fourth grade, they asked me what I wanted to be.
• 3:57
  And I said, either and a Supreme Court justice or the head of NASA.
• 4:03
  I always loved science as a child, and I was always fascinated with issues of justice.
• 4:08
  And so when I said that in the fourth grade, my teacher looked at me and was like, oh, this is impossible.
• 4:14
  You can't do both of these things.
• 4:15
  And it is true, you cannot be the head of NASA and a Supreme Court justice.
• 4:19
  But I think the spirit of both of those things are very much at the core of my work, looking at issues of justice and fairness in society, and also looking at how emerging technologies are shaping our society as well.
• 4:31
  You found a way to blend your two interests.
• 4:35
  I did.
• 4:35
  I was a stubborn child.
• 4:36
  I made it work.
• 4:40
  So looking back, was there one thing you wish someone had told you when you were our age, before you committed to any particular path?
• 4:48
  I think the biggest thing is life can be a little more forgiving than what you think, and that there's a lot of opportunity for experimentation.
• 4:55
  One of the things that was really shocking to me, especially at this age, is a lot of the jobs that I've done didn't actually exist when I was a kid.
• 5:03
  The idea of being a civil rights attorney focused on AI was like, I mean, 2005 would have been like, whoa, what are you talking about, right?
• 5:11
  And so I think being open to changes, being open to experimenting with different possibilities within your career, especially when you're younger, is so important.
• 5:20
  Don't feel like you have to make one decision and stick with it.
• 5:24
  I find that the folks who enjoy their work the best are those that give themselves license to just do and experiment with a whole bunch of different things.
• 5:32
  Things that make sense and are intuitive and things that don't sometimes as well.
• 5:37
  That really is the crux of this podcast.
• 5:39
  Trying to figure out how the barriers are changing.
• 5:43
  Yeah.
• 5:44
  Yeah.
• 5:46
  So before AI, how did systems like schools, courts, and employers make high-stakes decisions about people?
• 5:54
  And what specifically changed when algorithmic tools entered?
• 5:59
  Yeah, so these really important institutions like schools, employers, hospitals, so many others, when they're making the most kind of important or sensitive decisions about our lives, historically those decisions were reserved for human beings.
• 6:11
  So whether or not you were going to have access to health insurance or whether or not your kid was going to be able to enroll in a school, those decisions were always left to human beings.
• 6:22
  And that's because we understood the gravity and the sensitivity of those decisions.
• 6:26
  You're talking about choices that could fundamentally alter the trajectory of a person's life.
• 6:31
  But unfortunately, we've seen in recent years the kind of deepening of the use of artificial intelligence and algorithmic decision-making systems to replace human decisions.
• 6:42
  So essentially replacing human decision making with machine decision making.
• 6:46
  And there are a lot of harms that come from that.
• 6:48
  But from a civil rights perspective, if someone is making a decision about whether or not I have a job and they're using someone's race or gender or disability status to make that decision, that's unlawful.
• 7:02
  And as an attorney, we know we have tools to be able to respond to that.
• 7:06
  We can develop complaints to the Equal Employment Opportunity Commission.
• 7:10
  We can file lawsuits, we can engage in advocacy to hold those individual actors accountable.
• 7:15
  But when we shift that decision making from a human being to a machine, it becomes much more difficult to hold them accountable because we don't really have comprehensive legal frameworks in the United States that specifically address the harms and consequences of artificial intelligence and automation.
• 7:33
  We do have long-standing protections under the Constitution, under federal law that protect our basic rights.
• 7:39
  But unfortunately, we don't have a lot of experience collectively yet applying those historical protections to these new and emerging contexts.
• 7:48
  And so that is creating a gap that we're seeing around the country where powerful corporations or powerful government agencies are deploying these technologies, but they don't actually have the experience, the knowledge, the know-how in terms of actually protecting our rights against those machines when they are in fact violating our rights.
• 8:09
  So you've mentioned how AI can change the trajectory of someone's life.
• 8:14
  You've investigated schools that share students' abuse history, grades, and attendance records with law enforcement to build predictive policing systems.
• 8:24
  Can you walk us through what that looks like in practice and what that means for a student who ends up in that database?
• 8:32
  Yeah, I mean, you know, this is um it's one of the most egregious practices that I've seen around the country.
• 8:38
  For folks who aren't familiar, predictive policing is basically when law enforcement agencies or any other agency for that matter takes a variety of data about an individual and uses it to develop a forecast for that person, basically determining whether or not they think that person is going to commit a crime in the future.
• 8:58
  And you're policing them, criminalizing them, punishing them, surveilling them before they commit the crime.
• 9:04
  That goes against every notion of fairness that we have, right?
• 9:07
  To simplify it, imagine you come home one day on a Monday and your parents like you're grounded, and you're like, Well, what did I do?
• 9:14
  I think you you might you might do something on Thursday.
• 9:17
  So I'm gonna go ahead and preemptively ground you now, uh, even though you haven't done anything wrong, because I know the last couple of weeks you've been acting up.
• 9:24
  That is so unfair and unjust, right?
• 9:26
  If a parent does it, so imagine what happens when law enforcement or courts or schools do that, right?
• 9:32
  And that's what we saw in this particular community.
• 9:34
  They were taking the most sensitive information about a child's life, their graves, they were looking at attendance records, whether or not they had been abused as children, whether or not they had been themselves a victim of crime, looking at all of this information, taking it together, feeding that data to an algorithm in the sheriff's office to make predictions about which kids were likely to develop into criminals.
• 9:57
  And they then used that information, they used those scores as a basis to surveil, harass, um, and essentially try and intimidate those kids and their families out of the community, right?
• 10:09
  One officer went on the record to say that the goal of that program was to put so much pressure on those families that they would either move or sue the county, which is horrific to think about.
• 10:20
  From the child's perspective, from the student's perspective, they were being subject to unrelenting harassment from school-based police as well as police and the communities.
• 10:30
  They're coming to their homes at all hours of the day at night, knocking on the doors, looking through the windows, issuing civil citations.
• 10:38
  These are fines and fees for having your grass too high, or the numbers on your mailbox aren't properly displayed.
• 10:44
  Again, using all of these tactics and tools to try and harass these families because their kids, they believe, would commit crimes in the future and they wanted to run them out of the community now.
• 10:55
  Because of the data sources that they were pulling from, kids from marginalized communities were the ones who were being targeted by this program, right?
• 11:02
  Kids who actually had been failed by systems, kids who had not been protected by systems were now being targeted by those very same systems, right?
• 11:10
  And the consequences were devastating.
• 11:12
  We found that over 17,000 kids in this community had their data accessed by law enforcement, and that there were hundreds of kids that had secret intelligence dossiers.
• 11:22
  They kept secret files on many of these kids detailing about who their friends were and what was going on in their homes and what was going on with their families, keeping these really intimate data portraits about these young people.
• 11:33
  And they were sharing this information with federal law enforcement agencies, everybody from FBI to U.S.
• 11:39
  attorneys' offices.
• 11:40
  And we had really strong reason to believe that kids who were placed on this list were facing higher risk of suspensions, expulsions, arrests, and in a couple of cases, involuntary psychiatric detention.
• 11:53
  In this state, they had the ability under state law to put kids into these involuntary psychiatric detention for up to 72 hours without their parents knowing or being notified.
• 12:05
  So that was a consequence for the young people.
• 12:07
  But the beauty of that is when the community learned about this program, everybody came together.
• 12:12
  Local advocates, teachers, parents, civil rights attorneys, policy advocates, researchers, experts, everybody came together to really raise the alarm and to successfully fight in that program.
• 12:25
  Well, it sort of goes against the whole principle of you're innocent until proven guilty.
• 12:31
  Exactly.
• 12:31
  It flips it on its head, you're guilty until we can prove you innocent.
• 12:35
  And you don't even know.
• 12:36
  The scariest thing about that program was those kids had no idea that they were on it.
• 12:40
  They just all of a sudden have police asking them questions, calling them into the office, visiting them at their homes, like doing all this stuff to them and their families, and they have no idea why that's happening.
• 12:51
  Again, these are some of the most vulnerable families that need access to resources, but you have the police functioning as a barrier to being able to access those services.
• 13:01
  And I doubt it helps the kids not act out at all.
• 13:05
  In fact, they had a claim that they designed this program as a mentorship program so that the kids would have access to adults that cared about them and law enforcement to just check in on them.
• 13:15
  And they had a list of community resources they would pass out to the kids.
• 13:20
  And one of the listed resources was actually a member of a coalition that was fighting against the program.
• 13:24
  And so we asked, Have y'all ever talked to the sheriff's office about this or had somebody referred to you from the sheriff's office?
• 13:30
  Like we never knew about this at all.
• 13:32
  We've never had a kid referred to us.
• 13:34
  And as far as we know, none of the kids that we serve have ever actually, you know, experienced this program in the way that they're describing it.
• 13:41
  So yeah, it was all kind of a hoax so that they could surveil and harass kids so they wanted to get about the community.
• 13:47
  You co-wrote a piece in Fast Company arguing schools should abandon facial recognition entirely.
• 13:54
  In episode one with Nathan Wessler, we talked a little about privacy and facial recognition.
• 14:00
  Why is facial recognition in schools different from other surveillance?
• 14:06
  Yeah, what makes facial recognition particularly harmful is number one, real-time facial recognition is a pretty much a fraudulent technology, right?
• 14:15
  Um the current state of science doesn't actually allow for it to do what it says that it can do.
• 14:20
  There are groups like Big Brother Watch, which is over in the United Kingdom, that's done some testing of real-time facial recognition systems, and they found error rates as high as 85%.
• 14:31
  That means 85% of the time they're getting it wrong.
• 14:34
  And that's because a lot of times the technology, when it's tested in the lab, those lab conditions are very different than the conditions in the real world, especially in a school, right?
• 14:44
  Imagine we're having this conversation.
• 14:46
  If you're just using a facial recognition for either one of us, it could be a little bit easier.
• 14:50
  We have good lighting conditions, right?
• 14:52
  We're squarely faced in front of the camera.
• 14:54
  We've all seen like footage from a school camera.
• 14:56
  There are hundreds, if not thousands, of kids walking through the halls.
• 14:59
  There's lighting may or may not be there in terms of being able to actually differentiate somebody's face.
• 15:04
  Where the camera's located can change.
• 15:05
  Sometimes kids have on hats, so their hair is in front of their all of those things make it very difficult for the system to be able to work in real time.
• 15:12
  And so what ends up happening is you get a whole bunch of false errors and false positives, false negatives, so on and so forth.
• 15:18
  And what ends up happening is schools get overwhelmed, right?
• 15:20
  You're getting all of these results, all of these outputs, and you're like, wait, what's real, what's not?
• 15:25
  When should we call law enforcement?
• 15:27
  When should we not?
• 15:28
  And it just creates a very disastrous and unmanageable situation for schools.
• 15:31
  So that's the technical issue.
• 15:33
  But then there are the legal and ethical issues as well.
• 15:35
  The Fourth Amendment pretty much prohibits this practice within schools.
• 15:40
  For folks who don't know, the Fourth Amendment creates a constitutional prohibition on unreasonable searches and seizures.
• 15:45
  What that practically means is that law enforcement or school officials cannot conduct a search of a child or student in a school unless they have a reasonable, articulable basis to believe that child has either broken the law or that they have broken a school policy.
• 16:00
  But without that reasonable suspicion, they can't search you.
• 16:04
  Um so when you install a facial recognition system and you have no reason to believe that particular child has committed a crime, then you're essentially going back to the point you made earlier, you're assuming that people are guilty and they have to prove themselves innocent.
• 16:18
  So this clearly violates the Fourth Amendment.
• 16:20
  And then there's the issue that I work on quite a bit, which is how this technology is an investment in the school-to-prism pipeline, that this is a tool that expands surveillance within schools, and that surveillance always has a disproportionate impact on marginalized student populations.
• 16:36
  That even if these technologies were able to work, when you deploy them in a schooling context where black and brown students, indigenous students are receiving discipline at multiple times the rates of their white peers, even though there's no evidence to suggest that they misbehave at higher rates, you are just essentially pouring fuel onto the fire of that school-to-prison pipeline and allowing it to rapidly proliferate.
• 16:59
  And all of this is driven by profit-driven companies, right?
• 17:04
  These are companies that are selling these technologies to schools.
• 17:07
  This is a$4.4 billion industry.
• 17:10
  And you're basically selling mass surveillance to school districts that systematically violate students' rights while making profits for private companies.
• 17:18
  It's really the worst, it's a worst case scenario for everybody.
• 17:23
  You've described what sociologist Ruha Benjamin calls the New Jim Code.
• 17:28
  The idea that AI can encode racism while looking neutral.
• 17:32
  What does that mean and how does that work?
• 17:37
  Yeah, the New Jim C.
• 17:41
  Benjamin.
• 17:42
  And going back to what I said at the beginning of the conversation, I'm from Alabama, and my grandparents were born under Jim Crow.
• 17:49
  And so when they were born, there was literally those whites-only signs and colored signs over restrooms, water fountains, in buses.
• 17:58
  What we're seeing today is a reimagining of that, that we're taking those whites-only signs or we're taking those colored signs and turning them into digital code.
• 18:06
  Um, and what is so scary about it is that it can do the same thing, right?
• 18:11
  That when we put an AI system in charge of making decisions about who's going to access housing opportunities or who's going to access educational opportunities or job opportunities.
• 18:21
  Um, what we have consistently found is that these systems are reproducing racial, racially biased outcomes.
• 18:29
  But oftentimes when we implement these systems, we implement them because we believe that they are a race-neutral alternative to human decision making.
• 18:37
  But the problem with that reasoning is humans design these systems, humans design these technologies, and humans are using and deploying these technologies.
• 18:47
  So you can't really take the social context out of the machine, either on the front end or on the back end.
• 18:54
  And that is a challenge that I think we're collectively facing right now because we're seeing, as I mentioned, these technologies reproduce the same type of racially disparate outcomes that we saw historically when my grandparents were alive.
• 19:07
  But at the very least, you could see the signs and you could see who was putting them up, and you could see, and you could find ways of being able to challenge that.
• 19:14
  But because there's this opacity, there's this lack of clarity, there's this lack of transparency around how these technologies are now being used to affect the most sensitive decisions about us, we're seeing the same results, but it's much more difficult for us to challenge.
• 19:30
  You've worked on AI bias and hiring, where a company uses algorithms to screen job applicants.
• 19:37
  Say I'm a student applying for jobs next year.
• 19:40
  What might an algorithm be doing to my resume before a human even sees it?
• 19:45
  Would I even know?
• 19:47
  Yeah, that's the scariest part.
• 19:49
  In many places around the country, there are really no laws that require companies to disclose the use of AI or algorithmic decision-making systems when it comes to hiring.
• 19:59
  So we're seeing companies around the country who are integrating AI into their resume screening.
• 20:04
  So there may be the reality is if you're applying for a job in today's economy, you may be applying for a job and no human being actually ever sees your application, that it's being entirely screened out by an AI system.
• 20:16
  And again, there's really no, at the at least at the very least at the federal level, there's no regulation or law that kind of stops or prohibits a company from doing that.
• 20:26
  And because we don't have rigorous standards for testing these systems, it could be very well the case that these screening tools are using impermissible characteristics, which is just a fancy legal way of saying that they are looking at somebody's race or gender or disability status and using that to screen people out.
• 20:45
  In the law, we have this thing called proxy discrimination.
• 20:48
  That's just a fancy way of saying that sometimes they're not necessarily using gender explicitly, but they're saying that people with long hair or that somebody who was a part of a women's organization cannot or should not get this opportunity.
• 21:02
  And that's one thing that we're very worried about because AI models are evaluating hundreds, if not thousands, or even beyond that, tens of thousands of potential data points.
• 21:13
  And they're clustering them together in these neural networks to try and figure out, again, like who's going to get this opportunity and who's not.
• 21:20
  What that practically means for us is there's a whole bunch of decisions that could potentially be using a proxy characteristic, a proxy for race, a proxy for gender, a proxy for disability status that we have no idea because of the inscrutability, right?
• 21:36
  The inab our inability to actually evaluate these systems to see how they work underneath the hood.
• 21:41
  And yeah, like I said, there's a good chance that your resume, your application doesn't get seen when it does get seen by a human, even if you're going through the interview process.
• 21:50
  There are AI systems that are integrated into hiring platforms now, so that if you're doing an interview on the screen like we are, you could have biometric systems.
• 22:00
  Operating in the background that are measuring the words that you're using, measuring your tone of voice to try and make inferences about your character and whether or not you're fit for this particular role.
• 22:10
  Are you happy enough?
• 22:11
  Are you smiling?
• 22:12
  Is your voice high enough?
• 22:13
  All of those subjective characteristics are being evaluated by an AI system and being fed to the employer to see whether or not you're going to get this opportunity.
• 22:23
  And even if you get past that and you get the job, there's a bunch of worker surveillance technologies that we're seeing across industries that are measuring people's productivity, measuring your movements, measuring what you're typing over the course of a day.
• 22:36
  And so we're really seeing AI integrated across the entire life cycle from application to being on the job, and it's really making people's lives much more harder than what it was years ago.
• 22:50
  Companies and school districts often say that these tools are just neutral data.
• 22:55
  They don't see the discrimination and bias.
• 22:58
  What is wrong with that argument?
• 23:01
  Yeah, I mean, it's similar to what I said before that these technologies are designed in a social context, they are deployed in social context, they are governed in a social context.
• 23:12
  And you cannot separate the social context from the technology.
• 23:16
  And so this idea of AI is colorblind is really a foolhardy idea that we've seen.
• 23:23
  I mean, even if you don't want to do all the rigorous analysis about the socio-technical implications and all that other stuff, you can just look at the headlines, right?
• 23:31
  Every other day it feels like there's a news story about AI discriminating against students with disabilities or AI creating harms in the housing markets or AI discriminating against people who are looking for job opportunities.
• 23:43
  And so we see these bias outcomes repeat themselves over and over and over again.
• 23:48
  And it's becoming very clear that these systems are a part of this infrastructure of inequality within our society, and that we have to do something immediately, as soon as possible, to try and rein in some of these abuses and harms because they do threaten to re-reinforce and to re-re-constitute those systems of segregation and racial inequality that I've been talking about throughout today's conversation.
• 24:12
  It makes you question is the world moving forward?
• 24:16
  Right.
• 24:19
  So, what's the biggest barrier right now to holding AI systems accountable when they cause harm legally, politically, or practically?
• 24:34
  Yeah, I think you know, one of the biggest barriers is that policymakers have failed to do their job to really update civil rights for uh the era of AI and big data.
• 24:44
  There have been a couple of states that we've seen and localities that have passed law that expand anti-discrimination protections in the context of AI, places like Colorado come to mind.
• 24:55
  But at the federal level, we're really not seeing anything, and we don't expect anything from this Congress that addresses AI and civil rights.
• 25:02
  And states are trying, but many states just don't have knowledge, expertise, so on and so forth to be able to develop these protections.
• 25:10
  Even beyond the legislatures, right?
• 25:12
  There are, as I mentioned before, existing laws that already apply to these systems.
• 25:17
  And we're seeing state, state policymakers that are in charge of enforcing these laws, they're also failing on their responsibility in part because they don't know how to enforce these standards.
• 25:27
  They're not taking the steps needed to make sure that our rights are being safeguarded.
• 25:32
  So we need policymakers and others to step up and do their job.
• 25:35
  The thing that's encouraging is that we're seeing a lot of community groups, we're seeing a lot of advocacy organizations and others who are trying to step up and fill in the gap, who are working with communities to investigate these harms, to understand their impact on vulnerable and marginalized folks and developing really innovative strategies to create accountability.
• 25:54
  But, you know, community groups, advocacy orgs, like we can't do it on our own.
• 25:57
  Like we need those policymakers to really step up at the state and federal levels to make sure that people's rights are being protected.
• 26:05
  So looking 10 to 20 years ahead, AI is getting faster, cheaper, and harder to audit.
• 26:11
  What does the world look like for a student just starting out today if nothing changes?
• 26:16
  And what could change if the right protections get built?
• 26:22
  Yeah, you know, I think that's such a great question.
• 26:25
  I think looking 20 years out, a couple of things come to mind.
• 26:28
  So first, if we don't do anything to rein this stuff in, um, I do think we're going to see the creation of a digital underclass, an AI underclass.
• 26:37
  People who are algorithmically scored and screened out of opportunities, who are not going to be able to get the housing opportunities they need, the employment opportunities.
• 26:47
  And I think that is uh is very disturbing and startling to me.
• 26:51
  I think we'll also continue to see an erosion of basic civil and human rights, that you'll be able to use AI effectively as a shield to discriminate at will.
• 27:00
  And I think again, the collective consequence of that is a society in which sexism and ableism and racism runs rampant and it's mediated again through these digital technologies.
• 27:10
  And I also think, quite frankly, we'll see a systemic erosion of public goods and services, right?
• 27:17
  One of the things we hadn't had a chance to talk about, but that really concerns me is how AI is being used to replace basic goods and services.
• 27:26
  We're seeing interest in replacing teachers with AI, replacing judges with AI, replacing doctors and medical decisions with artificial intelligence.
• 27:36
  And that's being pushed at the highest levels of government right now.
• 27:40
  And I would hate to see what an AI school looks like or an AI doctor chatbot looks like.
• 27:46
  And I think that's the reality that we're speeding towards without these regulations.
• 27:50
  I think there's also a very high likelihood that this bubble around AI burst and we could see another Great Recession or Great Depression because all the things that these companies promise that AI can do, it simply cannot, right?
• 28:05
  AI is not going to be the great uh displacer of human workers, right?
• 28:10
  Like it's they hype it up intentionally.
• 28:12
  It's really like WWE, right?
• 28:14
  If you listen to like a WWE wrestler and you listen to an AI tech CEO, they sound very similar in terms of how they promote and hype their products.
• 28:23
  And I think that's putting our economy on a crash course for when that bubble bursts.
• 28:27
  So all of those things are potential futures that we're facing.
• 28:31
  But I think if we get this right, if we rein these technologies in, if we create greater accountability mechanisms for these companies, we expand the ability for communities to democratically control these technologies.
• 28:43
  I think the vision of that looks like NASA, right?
• 28:46
  I oftentimes think about the difference between a NASA and a SpaceX, right?
• 28:51
  The difference between what happens when public, when the public comes together to control and to govern how science and innovation happens in society.
• 28:59
  We literally can go farther than humanity has ever traveled.
• 29:02
  Meanwhile, when we try to hand those same, we try to hand over innovation and place it squarely in the hands of billionaires and other corporate interests.
• 29:11
  They literally can't get rockets that don't explode, right?
• 29:14
  We've seen how many times have we seen a SpaceX blow up at the launch pad.
• 29:17
  And so those are the two, I think, contrasting visions.
• 29:20
  We can explore new opportunities, new depths of humanity that we've never been able to before, or we can just see a whole bunch of people get richer as their products blow up in our faces.
• 29:28
  I think that's the stark reality that we're facing right now.
• 29:32
  And the question is, which way do we go?
• 29:34
  Exactly.
• 29:36
  You mentioned briefly your concern that AI is gonna take these jobs.
• 29:40
  Our listeners are deciding right now what to study and what to pursue.
• 29:45
  What would you tell a student whose word that their careers are excited about today might not exist in 20 years?
• 29:55
  Yeah, it's tough.
• 29:56
  I mean, um, there's two things I would say.
• 29:58
  One is we've seen a trend around automation displacing workers that dates back really to the mid-20th century.
• 30:05
  And we've seen the impact of that in places like the Midwest and the industrial heartland of this country.
• 30:10
  Um, so I think the force of automation as a um, I guess we we've seen what automation looks like as a force of job displacement at a very large scale.
• 30:22
  And I think that um both AI and a lot of these other emerging technologies um do present the risk of accelerating those trends.
• 30:29
  So I would say that, and I would say like it's a real concern.
• 30:32
  I would also say that, again, just be mindful of the hype and understand that a lot of the like 90% of jobs are going to be replaced by AI is very much industry propaganda that has no basis in reality.
• 30:45
  There's a bunch of um companies in the last two years that fired a lot of uh coders and engineers, right?
• 30:53
  Um, like to the tunes of like tens, if not hundreds of thousands.
• 30:56
  And they've quietly in the last year or so had to hire back thousands of those people because they realize that AI is absolutely terrible at being able to develop and maintain the codes that these companies rely on to make their profits.
• 31:10
  Um we've seen that in a number of other sectors as well.
• 31:12
  So I would tell young people like pursue your passions, develop them, develop your gifts and your skills and your talent.
• 31:19
  Because at the end of the day, an incredibly talented person pursuing their passion, you can't replace that with a machine.
• 31:26
  There's just no way that you can.
• 31:27
  And I think that's the way that you industry proof your career path.
• 31:31
  Um, so I would say be mindful of these threats, be on the lookout for, hey, I want to do this particular job, and it's looking like the industry is actually displacing, right?
• 31:41
  In that case, you might want to think about what are alternative career paths.
• 31:44
  But I think for the overwhelming majority of people, you pursue your passions, you develop that skill, develop those talents.
• 31:49
  I think you'll be able to go far.
• 31:52
  For listeners who think that your work, civil rights law, tech policy, community advocacy, it sounds exactly like what they wanted.
• 32:01
  What would you tell them?
• 32:07
  Yeah, I would say this is a time where this field is so new and it's rapidly growing and expanding.
• 32:13
  So I would say whatever piques your interest, reach out, connect with people, learn more about their work and their career path.
• 32:20
  A lot of the people I know who do this work, um, again, none of us knew that this job would even exist um 10 years ago or 15 years ago.
• 32:27
  And so a lot of people have come from a variety of backgrounds and found their way into this space.
• 32:33
  So if there are things that are happening locally, if there's a fight locally against a data center, you know, that could be a great opportunity to volunteer and get engaged with your community.
• 32:42
  If there's a local researcher that's looking at the use of artificial intelligence to regulate access to healthcare or to education or so on and so forth, reach out to them and engage.
• 32:53
  People are um a lot more friendly than what you think and are willing to make time to talk about them and their work.
• 32:58
  Um and then also explore it in your own life and in your own communities, right?
• 33:02
  You can initiate stuff yourself and be like, hey, I want to do a paper on this, or I want to put together a complaint to challenge the use of this harmful system in my school.
• 33:12
  Um and reach out to advocates or reach out to organizations that work on that and be like, hey, I want to lead this.
• 33:17
  Can you all support me?
• 33:18
  Can you all provide resources?
• 33:20
  And again, you'll be surprised at how often folks are willing to step up.
• 33:23
  I'll leave you with this.
• 33:24
  Ella Baker had this quote that she said, um, not Ella Baker, um, who said this was Mary and Wright Edelman.
• 33:31
  Mary Wright Edelman had this beautiful quote that I heard years ago.
• 33:34
  She said, um, that God runs a full service economy, and that if you follow the need, you'll never be without work.
• 33:40
  And that's what I've led with um, that's how I've led my career is by following that wisdom.
• 33:45
  But if you follow the need, you follow where the harm is happening, where the hurt is happening, and you show up with helping hands, you'll never be without work.
• 33:54
  What's one unexpected skill that's been crucial to your work?
• 34:00
  Creativity.
• 34:01
  Creativity and art.
• 34:03
  Um, I'm somebody who lawyer by training and don't necessarily think about kind of art and culture and creativity as the heart, the heartbeat of advocacy, but it's so essential, especially on an issue like artificial intelligence and civil rights, both of those things are very dense and daunting subject matter.
• 34:20
  Um, and a lot of times the communities that are experiencing injustice, like they don't want to engage, not because they don't care, but because they're overwhelmed and it's hard to make sense of what's happening and understanding the technology and understanding the law.
• 34:34
  And art has this really powerful ability to bridge the reality of injustice with advocacy to respond to it.
• 34:43
  I have seen that uh up close and personal.
• 34:45
  For um, I'm a co-founder of the Notice Coalition, which stands for No Tech Criminalization Education.
• 34:51
  So, national network of researchers, advocates, and organizers who were fighting to end youth surveillance and algorithmic injustice in schools.
• 34:58
  And one of the things that we have done in recent years, we've had an artist in residency program and we've done a community fellowship program.
• 35:06
  And both of those have produced amazing art, um, visual art, performance art, um, storytelling, like all these different artistic tools.
• 35:16
  And I have seen how when communities engage with that art, all of a sudden the issues make sense.
• 35:21
  We have, for example, a group of community fellows who put on a giant puppet show, like these massive puppets, life-sized puppets, to tell the story of surveillance in schools and how does it impact um young people.
• 35:33
  And I was blown away at how, like, we had done all of this research and talking at a conference, and people were kind of getting it, but not really.
• 35:41
  And they sat down and watched that puppet show and the light bulbs, you could just see it going off one after the other, and everybody really understood what was at stake, and they also could begin to see um alternative futures, how we could build just futures, and art really was that conduit, that pathway that allowed for communities to be able to understand and engage on these issues.
• 36:03
  That's so cool.
• 36:05
  I would never have expected the answer to be art.
• 36:09
  Same same with me.
• 36:11
  But it really makes sense.
• 36:15
  What is a book or podcast you would recommend for students who want to learn more?
• 36:22
  Oh gosh, this is so tough.
• 36:26
  We mentioned Aruha Benjamin at the top.
• 36:28
  Her book, Race After Technology, is absolutely fantastic and is a great read just to understand this issue and to really see the larger story of what's happening right now.
• 36:38
  That it's not just about these individual technologies, but this kind of larger societal transformation around the new Jim Code.
• 36:45
  But I would say in general, I've been really enamored with Afrofuturism in the last couple of years.
• 36:50
  And whether that's like watching Black Panther, whether that's reading Octavia Butler or N.K.
• 36:55
  Jemison, or whether even it's listening to music, I'm a millennial, so I'm a little older, but old school groups like Earth, Wind and Fire, all the way to Missy Elliott and Outcast, there's been this long-standing historical movement of black folks thinking about what does it mean to navigate the future, to build the future.
• 37:14
  And I think Afrofuturism is just such a really cool thing to play around with, especially as a contrasting vision of the future to what we're seeing from big tech and artificial intelligence.
• 37:24
  And I think anything really that allows for you to reclaim your imagination and to expand the way that you think about the future.
• 37:30
  AI is not going to define the future, our communities will.
• 37:33
  Anything that lets you get there is what you want.
• 37:37
  That is such a great final note.
• 37:40
  Well, Mr.
• 37:41
  Oka, thank you so much for joining us today on Paths of Curiosity.
• 37:46
  I loved all your insights.
• 37:48
  Thank you all so much for having me.
• 37:56
  This has been Paths of Curiosity in an AI world.
• 38:00
  Exploring careers through the lens of AI.
• 38:03
  Until then, stay curious.