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Q2 - Power, Law, and Civic Life: When Algorithms Govern Your Life Miniseries Recap · Paths of Curiosity in an AI World

• 0:04
  SPEAKER_00: Welcome to Paths of Curiosity in an AI world where we explore tomorrow's careers through the lens of AI.
• 0:17
  SPEAKER_00: A 17-year-old boy had never been arrested, never charged with anything, never convicted of anything, but he was in a secret intelligence file maintained by his county sheriff office, flagged as likely to become a criminal.
• 0:35
  SPEAKER_00: Because an algorithm said so, officers were showing up at his door.
• 0:40
  SPEAKER_00: His school was watching him, and he had no idea why.
• 0:45
  SPEAKER_00: This case was investigated by one of our guests this quarter, and when the community found out, 17,000 kids had their data in that system.
• 0:55
  SPEAKER_00: Hundreds had secret dossiers, some were placed in involuntary psychiatric detention for up to 72 hours without their parents being told.
• 1:09
  SPEAKER_00: Over the last several months, Acadia sat down with six people working at the place where AI meets power.
• 1:16
  SPEAKER_00: A surveillance attorney who argued before the Supreme Court and won.
• 1:21
  SPEAKER_00: A labor scholar who proved that two workers doing the exact same ride can be paid completely different amounts.
• 1:29
  SPEAKER_00: A legal technologist building deep fake detectors for courtrooms.
• 1:33
  SPEAKER_00: A UCLA professor who has spent decades asking whose values actually get built into the platforms that billions of people use every day.
• 1:43
  SPEAKER_00: A civil rights attorney who investigated that school surveillance program.
• 1:48
  SPEAKER_00: And a legal entrepreneur who is rebuilding how lawyers work from the ground up.
• 1:54
  SPEAKER_00: I'm Alora.
• 1:56
  SPEAKER_00: And I'm Acadia.
• 1:57
  SPEAKER_00: And this is the AI Empower Law and Civic Life mini-series recap.
• 2:09
  SPEAKER_00: Before we get into the guests, give me the honest version.
• 2:12
  SPEAKER_00: What's actually happening?
• 2:15
  SPEAKER_00: Every institution that governs your life, the courts, employers, schools, law enforcement, the platforms you use every day is now running some form of algorithmic decision making.
• 2:29
  SPEAKER_00: And most of the legal framework that are supposed to protect you were written before any of this existed.
• 2:36
  SPEAKER_00: Which means there's a gap.
• 2:38
  SPEAKER_00: A real one.
• 2:39
  SPEAKER_00: And what struck me across all six conversations is that none of these guests are anti-technology.
• 2:46
  SPEAKER_00: Every single one said the problem isn't a tool.
• 2:49
  SPEAKER_00: It's the absence of accountability around that tool.
• 2:53
  SPEAKER_00: Nathan Fraith Wessler made a point, one that reframes everything that follows.
• 2:59
  SPEAKER_02: There's practical impediments that meant it was very hard to violate lots of people's privacy at scale.
• 3:05
  SPEAKER_02: Of course, there have been terrible abuses of police power throughout this country's history.
• 3:09
  SPEAKER_02: So I'm not trying to minimize that, but it was very hard to do it in a kind of truly dragnet way.
• 3:14
  SPEAKER_02: Now these digital age technologies mean that police can collect those kinds of details on a person's life and so much more, literally at the press of a button.
• 3:25
  SPEAKER_00: So what changed isn't the government's desire to watch people.
• 3:29
  SPEAKER_00: It's the cost.
• 3:31
  SPEAKER_00: Exactly.
• 3:33
  SPEAKER_00: Let's talk about Carpenter.
• 3:35
  SPEAKER_00: The Supreme Court had to decide does the government need a warrant before accessing your cell phone location history?
• 3:43
  SPEAKER_00: Before that ruling, the government said no because the data is held by a third party.
• 3:48
  SPEAKER_00: You have no constitutional protection.
• 3:51
  SPEAKER_00: Nathan's argument, which the court accepted, was that the rule cannot survive in the digital age.
• 3:58
  SPEAKER_00: You cannot opt out of a phone.
• 4:00
  SPEAKER_00: And once that location data exists, it maps everything.
• 4:05
  SPEAKER_00: But winning one case doesn't close that door.
• 4:08
  SPEAKER_02: And law enforcement agencies have realized that in the aftermath of the Carpenter decision that makes them go to a judge if they want to go to the cell phone company, maybe they can get around that by going and paying money instead of going to a judge.
• 4:20
  SPEAKER_02: And so that's a problem that we're now dealing with.
• 4:22
  SPEAKER_02: And in fact, we've seen at the federal level the Department of Homeland Security, particularly ICE, re-upping its purchase of access to that kind of location information without getting warrants.
• 4:32
  SPEAKER_02: Just this week, the director of the FBI revealed that the FBI is again buying access to that information, something it had stopped doing a couple years ago.
• 4:40
  SPEAKER_00: Same data, different route, no warrant.
• 4:46
  SPEAKER_00: And that's the pattern across the entire episode.
• 4:49
  SPEAKER_00: The law moves slowly, the technology doesn't.
• 4:52
  SPEAKER_00: Your face is being tracked by networks of cameras paired with facial recognition.
• 4:58
  SPEAKER_00: So technology gets it wrong more often for darker skinned people, women, and those who are younger and older.
• 5:06
  SPEAKER_00: We've documented at least a dozen wrongful arrests of innocent people who matched a facial recognition result.
• 5:16
  SPEAKER_00: Episode two shifted to something many of our listeners are probably already living: gig work.
• 5:24
  SPEAKER_00: Vina Duball coined the term algorithmic wage discrimination.
• 5:28
  SPEAKER_00: Two drivers, same platform, same ride offered, different pay.
• 5:35
  SPEAKER_04: Algorithmic wage discrimination is the idea that different people can get different wages for the exact same work based on an algorithmic determination of how much they should earn.
• 5:52
  SPEAKER_04: Traditionally, because of the women's movement and because of the labor movement, we have this concept of equal pay for equal work.
• 5:59
  SPEAKER_04: If I'm doing the exact same thing as you at the same time and with the same skill, I think that we should earn the exact same amount of money.
• 6:21
  SPEAKER_04: And simultaneously disrupts the idea that we have equal pay for equal work.
• 6:34
  SPEAKER_04: And the wages, the price that they are given, the labor price that they're given for each ride that they do, is unique to them.
• 6:43
  SPEAKER_04: You and I could be offered the same ride, and we will be given completely different amounts.
• 6:50
  SPEAKER_00: And there's no law that stops that.
• 6:53
  SPEAKER_00: These workers are classified as independent contractors, which means they fall outside the safety net the labor movement spent a century building.
• 7:03
  SPEAKER_00: No minimum wage protection, no unemployment insurance, no right to organize collectively.
• 7:14
  SPEAKER_00: And into a problem most people haven't started thinking about yet.
• 7:29
  SPEAKER_05: Somebody goes into court, family court, and they say, My husband's abusing me and the kids.
• 7:40
  SPEAKER_05: I have an audio tape of him on my phone threatening to lock us in the trunk and to drive the car into the lake.
• 7:48
  SPEAKER_05: I want you to give me sole custody of the kids and a protective order that he can't come within a thousand feet of any of us.
• 7:57
  SPEAKER_05: Here, Judge, let me play you the tape.
• 8:01
  SPEAKER_05: Now, how is that judge supposed to have any idea whether that tape is real or not?
• 8:09
  SPEAKER_05: Because the technology today is so good that it's almost impossible to tell if that's real or not.
• 8:18
  SPEAKER_00: And she said generating that audio would take minutes for free.
• 8:23
  SPEAKER_00: You have enough of her voice from a single conversation to clone it convincingly.
• 8:29
  SPEAKER_00: She does a test in her talks, shows audience ten images, five real, five fake.
• 8:36
  SPEAKER_00: No one does better than chance.
• 8:38
  SPEAKER_00: 5050.
• 8:39
  SPEAKER_00: We are already there.
• 8:43
  SPEAKER_00: Episode four pulled us back to the biggest frame of the quarter.
• 8:48
  SPEAKER_00: Ramesh Srinavasin, founder of the Digital Cultural Lab, he's traveled to nearly 80 countries asking communities what they actually want from technology.
• 9:00
  SPEAKER_00: His central question is whose values get built into these platforms?
• 9:05
  SPEAKER_03: There's no such thing as neutral.
• 9:06
  SPEAKER_03: I mean, anything, uh anything and everything is conditioned based on how we choose to see the world or how a technology quote unquote chooses, or its designer or the engineer who builds a learning algorithm uh chooses to order information.
• 9:30
  SPEAKER_00: And you can't have a democracy where there's no shared ground.
• 9:34
  SPEAKER_03: He connects that to the rise of authoritarian policy government, not as a partisan claim, but as what happens when algorithmic fragmentation breaks shared reality that he also asks these communities what they wanted from technology, and their answer I don't know if Silicon Valley expects it or not, or cares about it or not, but most people just want to live a life of basic dignity, you know.
• 10:01
  SPEAKER_00: To live a life of basic human dignity.
• 10:04
  SPEAKER_00: Episode five brought us back to where we started.
• 10:08
  SPEAKER_00: Clarence Oka is a civil rights attorney who investigated that school predictive policing program.
• 10:15
  SPEAKER_00: The sheriff's office was taking data from schools, grades, attendance, abuse history, and feeding it into an algorithm to predict which kids would become criminals.
• 10:25
  SPEAKER_06: They were taking the most sensitive information about a child's life, their grades, 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 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.
• 10:48
  SPEAKER_06: 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.
• 10:59
  SPEAKER_00: And the kids being flagged were the ones who had already been failed by the systems.
• 11:04
  SPEAKER_00: The same systems that hadn't protected them were now using that history against them.
• 11:10
  SPEAKER_00: When the community found out, everyone came together.
• 11:13
  SPEAKER_00: Teachers, parents, advocates, and they successfully fought the program.
• 11:19
  SPEAKER_00: When I asked him what the problem with this program was, he gave this analogy.
• 11:25
  SPEAKER_06: And you're policing them, criminalizing them, punishing them, surveilling them before they commit the crime.
• 11:32
  SPEAKER_06: That's the goal against every notion of fairness that we have.
• 11:35
  SPEAKER_06: Simplified, imagine you come home on a Monday and your parents like you're grounded.
• 11:41
  SPEAKER_06: I think you you might you might do something on Thursday, something preemptively ground you down, uh, even though you haven't done anything wrong because I know the last couple of weeks you've been acting up.
• 11:51
  SPEAKER_06: That is unjust.
• 11:54
  SPEAKER_06: Like if a parent does it, so imagine what happens when law enforcement or courts or schools do that, right?
• 11:59
  SPEAKER_00: Episode six was different.
• 12:01
  SPEAKER_00: Jennifer Leonard isn't litigating cases.
• 12:04
  SPEAKER_00: She's working on the institution of law itself.
• 12:10
  SPEAKER_00: But what struck me most was the tension between what Maura Grossman and Jennifer Leonard said about the future of the profession itself.
• 12:19
  SPEAKER_00: Maura raised the deepest question first.
• 12:22
  SPEAKER_05: The first question is will we still have a courtroom with judges and juries?
• 12:31
  SPEAKER_05: Because maybe we just submit the dispute to an algorithm that can look at all the cases from the last 50 years and make a prediction.
• 12:41
  SPEAKER_05: And we don't even need to have the judge.
• 12:43
  SPEAKER_00: That's the scary version.
• 12:45
  SPEAKER_00: Do we even need lawyers if the machine can do it all?
• 12:50
  SPEAKER_00: But Jennifer Leonard answered that directly.
• 12:52
  SPEAKER_00: And her answer was different.
• 12:54
  SPEAKER_01: I think AI will replace some lawyers, but I actually think that the number of lawyers could stay the same or grow for a few different reasons.
• 13:04
  SPEAKER_01: One is our system of justice and system of laws is so badly constructed that right now lawyers are only addressing a little under 10% of the total civil legal needs of the American population.
• 13:20
  SPEAKER_00: So Jennifer's saying there's not less room for lawyers.
• 13:23
  SPEAKER_00: There's more.
• 13:24
  SPEAKER_00: The work changes, but the need grows.
• 13:27
  SPEAKER_00: That should be reassuring.
• 13:30
  SPEAKER_00: Except Mora added something else.
• 13:32
  SPEAKER_00: Something honest.
• 13:34
  SPEAKER_05: I'm teaching law school this semester, and my students ask me, Am I gonna get a job?
• 13:42
  SPEAKER_05: Is it gonna look the same?
• 13:44
  SPEAKER_05: Am I gonna have a career five years from now with all this generative AI?
• 13:50
  SPEAKER_05: And to be really honest with you, it's the first time that I can't really give you a good answer to that question because none of us know.
• 14:00
  SPEAKER_00: So Jennifer's right.
• 14:02
  SPEAKER_00: There's opportunity, but more is also right.
• 14:05
  SPEAKER_00: It's genuinely unclear what that looks like.
• 14:09
  SPEAKER_00: Both things are true, and that's the real situation for anyone entering this field right now.
• 14:15
  SPEAKER_00: Excitement and uncertainty at the same time.
• 14:19
  SPEAKER_00: Let's talk about careers because I think that's where most people have the biggest blind spot about this field.
• 14:27
  SPEAKER_00: What do you mean?
• 14:28
  SPEAKER_00: Clarence Oka said directly.
• 14:31
  SPEAKER_00: A lot of jobs I've done didn't exist when I was a kid.
• 14:35
  SPEAKER_00: That's true for nearly everyone we've talked to this quarter.
• 14:38
  SPEAKER_00: The field moves faster than the training pipelines.
• 14:42
  SPEAKER_00: Which means the people doing this work are the ones who are already curious about two different things at once and found the place where they crossed.
• 14:51
  SPEAKER_00: And that's still happening right now.
• 15:03
  SPEAKER_00: Nobody planned for them at 17.
• 15:06
  SPEAKER_00: Which means fields are being defined right now, but will be fully established by the time students in high school today reach mid-career.
• 15:16
  SPEAKER_00: Let's go through the guests, because how they got there matters as much as where they ended up.
• 15:21
  SPEAKER_00: None of these paths were straight.
• 15:24
  SPEAKER_00: Nathan Friedwessler, not sure about law school, took a job as a field organizer at the ACLU, watched lawyers in courtrooms, went to law school planning post-9-11 cases.
• 15:36
  SPEAKER_00: There wasn't a permanent job in that area when his fellowship ended, took an opening in speech, privacy, and technology, a political science major with no technical background, international relations and feminist studies at Stanford, a public interest attorney representing taxi workers in San Francisco, went back for a PhD, started her field work in 2012, just as Uber and Lyft were launching to watch the whole model unfold.
• 16:07
  SPEAKER_00: She followed the workers she was already studying.
• 16:17
  SPEAKER_00: In her 30s, went back to law school full-time, became a litigator at a major New York firm, started working on technology-assisted document review because the firm had cases with millions of emails, met a Canadian, moved to Waterloo, ended up teaching law and ethics to computer science students because no one else was asking the right questions.
• 16:42
  SPEAKER_00: He is a perfect example for this conversation.
• 16:45
  SPEAKER_00: He studied engineering at Stanford, but was shaped just as much by music, hip hop, punk, activism.
• 16:52
  SPEAKER_00: He was a college radio DJ.
• 16:54
  SPEAKER_00: Then he worked at an AI startup in Amsterdam right after graduation.
• 17:00
  SPEAKER_00: That trip through India along the Silk Road, traveling for months with nothing but cassette tapes and film informed everything he studies now.
• 17:08
  SPEAKER_00: Not what algorithms can do, but what they do do to culture, democracy, to the experience of being human when the world is filtered through code.
• 17:21
  SPEAKER_00: Said in fourth grade he wanted to be both the head of NASA and a Supreme Court justice.
• 17:27
  SPEAKER_00: His teacher told him he couldn't do both.
• 17:30
  SPEAKER_00: But he was a stubborn child and made it work, the spirit of both.
• 17:35
  SPEAKER_00: Civil rights attorney focused on AI and emerging technology, and Jennifer Leonard, came out of college in the late 90s when liberal arts majors were going to law school, found it stressful, went to the City of Philadelphia's law department, went back to Penn Law to build their first professional development program, became chief innovation officer, left after COVID to start her own company.
• 18:00
  SPEAKER_00: She wishes someone had told her don't be afraid to take risks.
• 18:04
  SPEAKER_00: Her generation of lawyers stayed in a job that they didn't love because changing felt too dangerous, and spent time talking to people who aren't lawyers.
• 18:14
  SPEAKER_00: If you stay in any bubble, you get a warped picture.
• 18:18
  SPEAKER_00: There's something that connects all six of these paths.
• 18:21
  SPEAKER_00: They all found the crossing point between two things.
• 18:26
  SPEAKER_00: Nathan is an organizer who became a constitutional lawyer.
• 18:35
  SPEAKER_00: Maura is a psychologist who became a legal technologist.
• 18:39
  SPEAKER_00: Ramesh is an engineer who became a cultural critic.
• 18:43
  SPEAKER_00: Clarence is a lawyer who thinks in art and community organizing.
• 18:48
  SPEAKER_00: Jennifer is a litigator who became an entrepreneur.
• 18:54
  SPEAKER_00: None of them are just one thing.
• 18:56
  SPEAKER_00: And the reason they're valuable is precisely because they're not.
• 19:01
  SPEAKER_00: Don't prune your interest too early.
• 19:03
  SPEAKER_00: The combination that feels random right now might be exactly what makes you irreplaceable later with concrete skills.
• 19:12
  SPEAKER_00: What do you actually need to build?
• 19:15
  SPEAKER_00: Nathan Wessler said the ability to sit still and read for long hours.
• 19:20
  SPEAKER_00: Most of his days are legal research and writing.
• 19:24
  SPEAKER_00: The skill that enables everything else is the capacity to stay focused.
• 19:30
  SPEAKER_00: Vina Dubal said writing, and she said it with urgency.
• 19:34
  SPEAKER_04: And so writing is not just like output, right?
• 19:38
  SPEAKER_04: It's not just like you create, you need it to write an essay, and so you ask Chat GPT to write an essay, and then you have an essay.
• 19:46
  SPEAKER_04: It's actually itself a process that helps you to think critically in a way that is quite useful for policy, for teaching, for organizing.
• 19:57
  SPEAKER_04: And so I think one skill that I think is just so, so critical that I'm really concerned people don't have the desire to cultivate is writing and rewriting.
• 20:10
  SPEAKER_00: Writing is itself a process that helps you think critically in a way that is quite useful for policy, for teaching, for organizing.
• 20:19
  SPEAKER_00: Mora Grossman said flexibility.
• 20:22
  SPEAKER_00: Most people are rigid.
• 20:23
  SPEAKER_00: The people who can deal with change are going to do very well.
• 20:27
  SPEAKER_00: Clarence Oka said creativity and specifically art.
• 20:41
  SPEAKER_06: I have seen up close and personal for um I'm a co-founder of the Methodist Coalition, which stands for No Tech Communication Education.
• 20:49
  SPEAKER_06: So National Network of Researchers, Advocates, and Organizers, provide in use of advocating injustice in schools.
• 20:57
  SPEAKER_06: And one of the things that we have done in recent years, we've had a business program and we've done a community fellowship program.
• 21:04
  SPEAKER_06: And both of us have produced amazing art, visual art, performance art, and all these different artistic tools, and I have to see how communities engage with that art.
• 21:19
  SPEAKER_06: We have, for example, a bit of a community fellows who put up a giant puppet show, like these massive puppets, like puppets to tell us about schools and how it's going to pack young people.
• 21:31
  SPEAKER_06: And I was blown away at how like this research and talking about the conference, and people were commenting it, but not really.
• 21:40
  SPEAKER_06: And then you sat down and watched that puppet show, and the light bulbs, you could just see them going off one after the other, and everybody really understood what was at stake.
• 21:49
  SPEAKER_00: Jennifer Leonard said systems thinking and metacognition, knowing how you learn best.
• 22:00
  SPEAKER_00: The hardest of all.
• 22:02
  SPEAKER_03: Just encourage everybody and anybody to keep their eyes open and explore and be curious and look widely, look beyond, look in between.
• 22:11
  SPEAKER_03: Do not give into the polar polarization of cancel culture.
• 22:16
  SPEAKER_03: Do not give into these things where you're like, I'm you look like this, or you talk with this accent, so I'm just gonna dehumanize you.
• 22:24
  SPEAKER_03: This is a big trap.
• 22:26
  SPEAKER_03: So we can do so so so much better.
• 22:28
  SPEAKER_03: And so I personally am very dedicated to being in dialogue and support of any and all uh movements and figures who stand for that.
• 22:40
  SPEAKER_00: Not as a resume line, as a real practice of breaking the assumptions you didn't know you had.
• 22:47
  SPEAKER_00: None of these are narrow technical skills reading, writing, flexibility, creativity, systems thinking, perspective.
• 22:56
  SPEAKER_00: The tools are changing too fast for any specific technical skill to be the whole answer.
• 23:02
  SPEAKER_00: What doesn't change is the ability to understand why something works, communicate it clearly, and see how it connects to a larger system.
• 23:11
  SPEAKER_00: I want to name something before we get to the action piece.
• 23:15
  SPEAKER_00: The gap between who these systems harm and who has the power to change them.
• 23:20
  SPEAKER_00: The communities most affected by predictive policing, by algorithmic wage discrimination, by biased hiring tools.
• 23:29
  SPEAKER_00: These communities are not the ones building these systems.
• 23:33
  SPEAKER_00: And the legal tools that exist to challenge the harms were written for a different era.
• 23:39
  SPEAKER_00: The laws lag, the institutions lag, the technology doesn't.
• 23:45
  SPEAKER_00: And yet every guest is still in the fight.
• 23:50
  SPEAKER_00: Concrete, what do you actually do if this order has sparked something?
• 23:55
  SPEAKER_00: Clarence Oka said, reach out to local researchers and advocators.
• 24:00
  SPEAKER_00: If there's a fight in your community, over surveillance in schools, over algorithmic tools in your hospital or your courts.
• 24:07
  SPEAKER_00: That is an entry point.
• 24:09
  SPEAKER_00: People doing this work are often looking for help.
• 24:12
  SPEAKER_00: You don't need credentials.
• 24:14
  SPEAKER_00: You need to show up and ask.
• 24:17
  SPEAKER_00: Nathan Wessler pointed to the ACLU's Free Future blog, a regular post on the most pressing questions of privacy and digital rights.
• 24:27
  SPEAKER_00: Vina Dubal said, go to Labor Notes, read the publications, listen to the podcasts.
• 24:34
  SPEAKER_00: Jennifer Leonard said, network outside your bubble.
• 24:37
  SPEAKER_00: Find someone doing something genuinely different and ask how they got there.
• 24:41
  SPEAKER_00: And Ramesh Shunavasin, who said studying abroad changed everything for him.
• 24:46
  SPEAKER_03: Just go study abroad.
• 24:48
  SPEAKER_03: It's a great thing to do.
• 24:50
  SPEAKER_03: Do independent research so you can actually explore things more deeply.
• 24:54
  SPEAKER_03: And often universities have money, even these days, with the attacks on universities by the Trump administration.
• 25:00
  SPEAKER_03: A lot of universities have the money to fund independent research and things like that.
• 25:06
  SPEAKER_03: You know, in my case, what blew my mind and opened me up was moving to Amsterdam, traveled around India when I was like 20 years old.
• 25:13
  SPEAKER_03: And then the big thing that changed my life is I had gotten into MIT for graduate school.
• 25:19
  SPEAKER_03: I had this job in Amsterdam that I mentioned.
• 25:21
  SPEAKER_03: I traveled all the way from Amsterdam to Western China, backpacking with cassette tapes and physical film for six months almost.
• 25:31
  SPEAKER_03: And then I traveling on the Silk Road, and that just changed blew my mind, you know.
• 25:37
  SPEAKER_00: That's not about tourism.
• 25:39
  SPEAKER_00: That's about breaking your default assumptions.
• 25:43
  SPEAKER_00: I want to end with something Nathan Fried Wessler said that I've been sitting with since episode one.
• 25:50
  SPEAKER_00: Tell me.
• 26:12
  SPEAKER_02: We can do a few, you know, a few things to avoid that future.
• 26:17
  SPEAKER_02: One, of course, is to make our voices referred to lawmakers and the people setting policy.
• 26:23
  SPEAKER_00: We spent a quarter listening to six people doing exactly that work every day in courtrooms, in classrooms, in community meetings, in code.
• 26:34
  SPEAKER_00: The legal frameworks for the AI era are being written right now.
• 26:38
  SPEAKER_00: The tools that courts will use are being designed right now.
• 26:42
  SPEAKER_00: The policy decisions are being made right now by people, which means students who are curious have a place in this.
• 26:53
  SPEAKER_00: This has been passive curiosity in an AI world.
• 26:57
  SPEAKER_00: I'm Acadia, and I'm Alora.
• 27:00
  SPEAKER_00: Until next time, stay curious.