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

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

• 0:04
  SPEAKER_00: Welcome to Has a Curiosity in an AI world.
• 0:44
  SPEAKER_00: Yes.
• 0:54
  SPEAKER_00: She is also a professor of anthropology by courtesy.
• 0:58
  SPEAKER_00: Her research examines how digital technologies and shifting regulatory frameworks shape the lives of workers.
• 1:05
  SPEAKER_00: Especially as a precarious and low wage and gig economy job.
• 1:10
  SPEAKER_00: She is the recipient of numerous awards and grants, including a full right fellowship.
• 1:16
  SPEAKER_00: Her research has been cited in legal decisions internationally in the California Supreme Court.
• 1:22
  SPEAKER_00: Her commentary is regularly featured on the New York Times and the Washington Post and NPR and CNN.
• 2:11
  SPEAKER_00: Recently appointed general counsel of the American Association of University Professors and is currently completing a book on how low-income immigrants and minority workers navigate technology and regulatory change.
• 2:25
  SPEAKER_00: Dr.
• 2:25
  SPEAKER_00: Duvall, thank you so much for joining us.
• 2:29
  SPEAKER_01: It's a pleasure.
• 2:30
  SPEAKER_01: Thank you for having me.
• 2:32
  SPEAKER_00: Most of our listeners here have never heard the term labor law or stop to think about whether the apps on their phone might be doing something unfair to the people on the other side.
• 2:43
  SPEAKER_00: What does your work look like day to day?
• 2:46
  SPEAKER_01: That's a great question.
• 2:48
  SPEAKER_01: So I am a labor law professor.
• 2:52
  SPEAKER_01: I teach in a law school, and I'm also a lawyer and advocate.
• 2:59
  SPEAKER_01: On a day-to-day basis, I both talk to faculty who essentially are my clients or who are members of the American Association of University Professors about their relationship to their bosses.
• 3:16
  SPEAKER_01: So faculty in relationship to their employers.
• 3:19
  SPEAKER_01: I also talk a lot to workers who are low-wage, often immigrant workers, about their everyday life experiences and how the law either makes those life experiences better or worse.
• 3:36
  SPEAKER_01: And in addition to doing research and doing advocacy and lawyering, I do a lot of teaching.
• 3:43
  SPEAKER_01: So I teach people about what labor law is, about the history of labor law, about the politics of labor law, and how to be good laborer and employment lawyers in the future.
• 3:57
  SPEAKER_00: You're now one of the most recognized critics of some of the biggest tech companies in the world.
• 4:03
  SPEAKER_00: What sent you in that direction?
• 4:07
  SPEAKER_01: Yeah, so I have always sort of followed my moral compass, and um, you know, it's a privilege to be able to do that.
• 4:17
  SPEAKER_01: I think early on in my career, I was studying taxi workers in San Francisco, and my research on taxi workers and the kinds of legal and regulatory laws that affected their employment status or their lack of employment status led me to understand how certain aspects of labor law had long excluded people of color, women, domestic workers, agricultural workers, independent contractors, that there were all of these low-income folks who didn't have even minimum wage protections.
• 4:54
  SPEAKER_01: And while I was studying taxi work in San Francisco, circa you know 2012-2013, Uber and Lyft started in the city.
• 5:04
  SPEAKER_01: And they started offering workers really high prices and consumers really low prices, so really high wages, really low prices.
• 5:15
  SPEAKER_01: They weren't making any money, they were using venture capital to kind of draw people in, both as workers and as consumers.
• 5:23
  SPEAKER_01: And I knew that this was going to be in the long run a really bad business model.
• 5:29
  SPEAKER_01: And so I started studying what we now call platform work that's disseminated by apps, and that's kind of what led me to thinking about the relationship between technology and law and wages and work quality and the kinds of laws and regulations we need to better work our everyday lives, our ability to feed our families.
• 5:51
  SPEAKER_01: And in the process, I've become quite a critic of companies who are not high-road employers, companies that are not, you know, treating their workers well, either legally or um or morally.
• 6:04
  SPEAKER_00: I can't wait to dive into your work.
• 6:06
  SPEAKER_00: But first, most of us assume that successful people had a clear path from the start.
• 6:13
  SPEAKER_00: Can you walk us from where you were as a student and what you thought you wanted to do all the way to where you are now?
• 6:21
  SPEAKER_01: Sure, it's a really good question.
• 6:23
  SPEAKER_01: You know, I as a student, I didn't have a clear vision of what I wanted to do.
• 6:28
  SPEAKER_01: I actually remember even in college thinking, gosh, I want to have purpose in life.
• 6:33
  SPEAKER_01: I want to make sure that whatever it is that I do has an impact on other people.
• 6:39
  SPEAKER_01: I want to make the world a more just and equal place, but I didn't really know how that was going to happen.
• 6:47
  SPEAKER_01: And a mentor of mine at some point said to me, Things just fall into place if you continued to follow your passion and work with integrity and work with the intention of fighting for a better world for others.
• 7:02
  SPEAKER_01: And so it wasn't clear.
• 7:04
  SPEAKER_01: There were a lot of serendipitous moments.
• 7:06
  SPEAKER_01: There was a moment in graduate school where I thought, oh my gosh, I don't know if I want to be an academic.
• 7:12
  SPEAKER_01: And I actually stopped out of graduate school and practiced law for a period of time.
• 7:17
  SPEAKER_01: I practiced public interest law.
• 7:19
  SPEAKER_01: And when I went back to the academy, I really kind of wasn't sure if I would succeed, if it was possible, if I could make it.
• 7:27
  SPEAKER_01: And so that along the road, there's been a lot of self-doubt, a lot of questioning, a lot of being unsure about what the future holds.
• 7:35
  SPEAKER_01: And if I were to advise my own children or folks who are listening today about how to move in the world, it would be you have to be realistic, certainly, about what opportunities exist and don't exist, but it's also possible to pave your own path and to have kind of the self-confidence and fire to know that is possible and to fight for it.
• 8:02
  SPEAKER_00: That sure is a journey.
• 8:05
  SPEAKER_00: Before algorithms, how did companies like Uber and DoorDash decide what to pay their workers and what fundamentally changed when AI took over?
• 8:16
  SPEAKER_01: That's a great question.
• 8:17
  SPEAKER_01: So, in the whole history of the relationship between firms and workers, the history of modern capitalism, wages have been determined by contract.
• 8:30
  SPEAKER_01: So employers and employees engage in a negotiation and they determine whether someone is going to be paid by the hour, whether they're going to be paid by the piece, whether they're going to be paid by the day, by the week.
• 8:46
  SPEAKER_01: In the transition to industrial capitalism, workers had very little power.
• 8:52
  SPEAKER_01: They were not able to be equal partners in this negotiating over price, wage price, and there emerged from this very little power that atomized workers had, wage labor movements where workers sort of fought together to say, withhold their labor and say, listen, boss, if you don't listen to us, if you don't engage in an actual negotiation with us over our wages, then we're going to strike, we're not going to work, and you're not going to have the product that you need to sell.
• 9:26
  SPEAKER_01: And so in this process, it became that workers fought for what we call a minimum wage.
• 9:32
  SPEAKER_01: They said, okay, there's an agreement that it was important to have control over your time.
• 9:39
  SPEAKER_01: And if you controlled your time and you ascribed a wage to an hour or to a day, then you could also control how much money you made over the period of a month.
• 9:51
  SPEAKER_01: And then you could control whether or not you could pay your rent, you could have a budget, you could allocate certain amounts of money to groceries, certain amounts of money to rent, certain amounts of money to luxury, to family, to clothing, etc.
• 10:07
  SPEAKER_01: And so in the Fair Labor Standards Act during the New Deal, one of the things that workers fought for amidst all of the precarities of the Great Depression and the extraordinary kind of hunger and houselessness that resulted during the Great Depression, a worker said, you know what, we want a federally mandated minimum wage.
• 10:28
  SPEAKER_01: And so the Fair Labor Standards Act got us in 1938 a federally mandated minimum wage, which means that workers have to earn at least a certain amount per hour.
• 10:40
  SPEAKER_01: And then over 40 hours a week, you get one and a half times that.
• 10:46
  SPEAKER_01: This was important for what we call work spreading.
• 10:49
  SPEAKER_01: There were you know really bad employers who made workers work 80, 90, 100 hours a week, and the result was that you they didn't have to hire two workers, they just had to hire one worker.
• 11:03
  SPEAKER_01: So the point of overtime was to incentivize employers to hire more people, to pay them enough so that they could also be consumers, so they could pay their rent, pay their groceries, maybe even buy a car, other basic necessities.
• 11:19
  SPEAKER_01: And that's the capitalism that we've had over the course of the 20th century and the beginning of the 21st century.
• 11:28
  SPEAKER_01: There have been, since I would say, the 1980s, a growing number of workers who are what we call misclassified, which means essentially that their employer pretends that they are not employees and that they're independent businessmen, independent business people.
• 11:49
  SPEAKER_01: And if they're independent business people, then the minimum wage doesn't apply to them.
• 11:54
  SPEAKER_01: We've seen this in the agricultural industry where you have farm owners pretending that the people that pick their crops are independent entrepreneurs and don't need the minimum wage protections.
• 12:06
  SPEAKER_01: You've seen this in the nail salon industry where nail companies sometimes lease a particular chair to a worker and say, you're your own independent business person.
• 12:18
  SPEAKER_01: We've seen it a lot in construction.
• 12:21
  SPEAKER_01: And this was my work early on, you see it in taxi work, where in the 1980s taxi companies essentially de-unionized the entire taxi industry across the country by saying these taxi workers are actually not employees, and so they're not protected by employment and labor laws.
• 12:39
  SPEAKER_01: They're independent business people, they're independent contractors.
• 12:43
  SPEAKER_01: Today, the whole system that we have of platform work, of AI, even the whole system of AI itself is built on independent contractor labor, the kinds of fictions that these workers are somehow small business people and don't need basic wage protections.
• 13:03
  SPEAKER_00: So legally, when a company calls workers independent contractors, not employees, why does that classification matter so much?
• 13:13
  SPEAKER_01: Yeah, it matters because if you're not considered an employee, then you don't get any of the safety net benefits that we have in the US and that all industrialized nations have.
• 13:26
  SPEAKER_01: So you don't get access to the minimum wage, you don't get protection against discrimination at work, you don't get unemployment insurance if you're laid off through no fault of your own.
• 13:38
  SPEAKER_01: And so this was a huge deal during the pandemic when so many people couldn't work.
• 13:44
  SPEAKER_01: People like me who are an employee, if I couldn't work or if my company laid me off, I could file for unemployment insurance.
• 13:52
  SPEAKER_01: This was a lifeline for so many people.
• 13:55
  SPEAKER_01: If you are an independent contractor, you don't have access to unemployment insurance.
• 14:00
  SPEAKER_01: If you're injured on the job, you don't have access to what we call workers' compensation, which is this little bit of money that the state owes to you if you're injured as a result of some kind of occupational hazard or occupational injury.
• 14:17
  SPEAKER_01: Most critically, you don't have access to protected organizing.
• 14:22
  SPEAKER_01: So when a bunch of independent, real independent businesses come together and they say, we want to fix wages at a certain amount, that is against the law.
• 14:36
  SPEAKER_01: It's against the Sherman Act, it's an antitrust violation.
• 14:39
  SPEAKER_01: Versus when bunches of workers get together and demand a minimum wage, that is protected by the National Labor Relations Act.
• 14:49
  SPEAKER_01: That's that's what it means to engage in collective bargaining to have a union.
• 14:54
  SPEAKER_01: And so in the mid-century, it was in, I think it was 1948, when the Congress passed a law that carved independent contractors out of all of these safety net protections.
• 15:08
  SPEAKER_01: And they said, if you're an independent contractor, you don't get all of these protections.
• 15:12
  SPEAKER_01: And at the time it didn't really matter a lot because most people were so were considered employees.
• 15:18
  SPEAKER_01: And over time, I think creative lawyers and business people thought, well, you know, how do we lower our labor overhead?
• 15:25
  SPEAKER_01: How do we lower the amount of money that we have to pay our workers?
• 15:30
  SPEAKER_01: Because then we can essentially keep more of our profits.
• 15:35
  SPEAKER_01: We can pay our executives more, we can give our shareholders more.
• 15:39
  SPEAKER_01: How can we lower our labor overhead?
• 15:42
  SPEAKER_01: And one creative idea was let's pretend we don't have workers, let's pretend actually we're just facilitating business, we're facilitating the ability of a lot of different people to work independently.
• 15:56
  SPEAKER_01: And that kind of, you know, what I would call that kind of legal trick did lower labor overhead significantly for many companies, but did so at great risk.
• 16:07
  SPEAKER_01: So when a company pretended like their workers were independent contractors, they were risking lawsuits, they were risking state enforcement.
• 16:18
  SPEAKER_01: So it wasn't going to be small businesses that did this or even medium-sized businesses that did this.
• 16:23
  SPEAKER_01: It's going to be businesses that are backed by a lot of capital that can deal with the litigation.
• 16:30
  SPEAKER_01: And so it really took venture capital, like the structuring of investment capital and the way that it's structured today, which hasn't always been possible.
• 16:40
  SPEAKER_01: It took venture capital, capital's investment in this kind of what I would say is a really not just unlawful business model, but an immoral business model for more and more workers to be treated as independent contractors.
• 16:55
  SPEAKER_01: And that's kind of like, you know, in the in the post-great recession era in the early 2010s, is when we saw this happen.
• 17:04
  SPEAKER_01: There was a lot of desire to not have bosses, which is, I think, an innate natural desire to not be subordinated or dominated by other people.
• 17:13
  SPEAKER_01: And venture capitalists, together with Silicon Valley founders, created this mechanism.
• 17:18
  SPEAKER_01: They said, okay, well, we're going to make everyone an independent worker, and we are going to pretend that they're not employees of ours.
• 17:28
  SPEAKER_01: And in doing so, we are going to start a whole new kind of work.
• 17:34
  SPEAKER_01: And then you got Instacart, you got Uber, TaskRabbit, Lyft, etc.
• 17:39
  SPEAKER_01: There were a lot of lawsuits challenging this, but because they had endless streams of venture capital money, they essentially settled all of these lawsuits.
• 17:49
  SPEAKER_01: So there was no real legal decision about the status of these workers.
• 17:53
  SPEAKER_01: And in the meantime, we had massive changes in cloud computing, all kinds of changes in machine learning systems that were enabled by the cloud computing technologies.
• 18:08
  SPEAKER_01: And this kind of space of independent contracting gig workers, as we call them, of Uber drivers, Lyft drivers, Instacart, and DoorDash workers, it became a site of labor experimentation.
• 18:21
  SPEAKER_01: So already these workers were not owed minimum wages, they weren't owed unemployment insurance, they couldn't have unions protect them.
• 18:30
  SPEAKER_01: And so the companies who were already remotely controlling their workers had free reign to experiment with all kinds of things, to experiment with remote termination.
• 18:42
  SPEAKER_01: So you don't have a boss that terminates you, but a machine learning system that determines that you're not worth the company's time anymore.
• 18:52
  SPEAKER_01: You don't have a boss that tells you where to go.
• 18:54
  SPEAKER_01: You have a machine learning system that tells you where to go and when.
• 19:25
  SPEAKER_01: These are all things that AI, at least companies, think they're capable of doing as a result of this unprotected labor space.
• 19:34
  SPEAKER_00: So essentially, these big companies are able to strip workers of all of their rights.
• 19:40
  SPEAKER_01: That's right, all of their rights and create new forms of control that are the opposite of what they promised.
• 19:47
  SPEAKER_01: They promised freedom and independence, and we're seeing the exact opposite.
• 19:52
  SPEAKER_01: Really, in there are advocates around the world who call this slave-like labor because the degree of control that companies have.
• 20:00
  SPEAKER_00: A lot of our listeners are already doing gig work, things like Instacart, TaskRabbit, and Lyft to save up or make spending money.
• 20:08
  SPEAKER_00: You coined the term algorithmic wage discrimination.
• 20:12
  SPEAKER_00: What does that mean?
• 20:14
  SPEAKER_00: And what is a desperation score?
• 20:17
  SPEAKER_01: Yeah.
• 20:18
  SPEAKER_01: So 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.
• 20:34
  SPEAKER_01: Traditionally, because of the women's movement and because of the labor movement, we have this concept of equal pay for equal work.
• 20:42
  SPEAKER_01: If I'm doing the exact same thing as you at the same time and with the same skill, in theory we should earn the exact same amount of money.
• 20:51
  SPEAKER_01: What algorithmic wage discrimination does is completely disrupt the idea that wages should be predictable and knowable and negotiated over between an employer and an employee, and simultaneously disrupts the idea that we have equal pay for equal work.
• 21:09
  SPEAKER_01: For an Uber driver, for example, how much they earn from hour to hour is unknowable to them.
• 21:17
  SPEAKER_01: And their wages, the price that they are given, the labor price that they're given for each ride that they do, is unique to them.
• 21:26
  SPEAKER_01: You and I could be offered the same ride, and we will be given completely different amounts.
• 21:32
  SPEAKER_01: Now, what goes into that decision making, why you get a different wage price than I get for the same ride, is again unknowable to us, but it is something that we could guess.
• 21:47
  SPEAKER_01: If we're talking about a company that's looking to lower labor overhead, you can imagine that given all the data that they have about you and I, where we live, how many kids we have.
• 22:00
  SPEAKER_01: What rent is in the area that we live in, what our monthly expenses are, how much we work over every day, and how much we earn over a given day before we stop working, they might be able to determine how little we might be willing to work for.
• 22:20
  SPEAKER_01: So again, like it's not clear that this is precisely what they're doing, but because everything happens behind a black box and it's how our wages are determined is knowable to us.
• 22:30
  SPEAKER_01: But the reality is that there is a great fear that, in fact, the companies are figuring out how desperate we are from moment to moment and accordingly, accordingly, you know, uh, paying us as little as we might be willing to receive.
• 22:47
  SPEAKER_01: There was a recent viral uh Reddit post that turned out to be a fall, turns out to be misinformation.
• 22:54
  SPEAKER_01: It was fake, but the term that was used in the Reddit Reddit post was a desperation score, that companies could use desperation scores to figure out how little to pay workers.
• 23:05
  SPEAKER_01: And you know, part of why it went viral is because it really made sense.
• 23:09
  SPEAKER_01: It vibed with people's experiences who do this work, and and given how exploitative these systems are, it made sense that maybe in fact these companies are assigning desperation scores to workers and allocating wages to us accordingly.
• 23:24
  SPEAKER_01: And it's important to note that there's nothing that would hold them back from doing that.
• 23:28
  SPEAKER_01: It's immoral, but there's no law or regulation that might prevent them from doing that.
• 23:34
  SPEAKER_00: In your essay, the house always wins, you use the words gamification to describe what these platforms are doing to workers.
• 23:43
  SPEAKER_00: What do you mean by that?
• 23:45
  SPEAKER_00: And why does that word matter?
• 23:47
  SPEAKER_01: Yeah, so this is actually a term that came from workers themselves.
• 23:51
  SPEAKER_01: They, in my long-term ethnography of Uber drivers, I've noted that the way that they experience their income has changed dramatically.
• 24:01
  SPEAKER_01: As I said earlier, their wages were very high in the early, in the early days and the two early 2010s, and over time they've diminished by 60, 80, 90 percent.
• 24:11
  SPEAKER_01: Um, and because they don't know how much they're going to earn from day to day, if they're gonna earn enough to to make ends meet, how long it's gonna take them to earn their their goal amount, because they don't know whether they're gonna be given a bonus or how much that bonus is going to be, or even if they're gonna have the opportunity, the ability to fulfill enough rides to get the bonus that they so desperately need.
• 24:35
  SPEAKER_01: For all of these reasons, they often they analogize their work to gambling.
• 24:40
  SPEAKER_01: That every day, because it's different, a lot a lot of your listeners are probably students, they're probably in some ways dependent on their family for support.
• 24:50
  SPEAKER_01: But if you're the primary breadwinner and 80% of the work that's done for Uber is done by people who are working full-time as Uber drivers or Lyft drivers, if you're the primary breadwinner and you can't make these like wage determinations, then going to work feels like a gamble.
• 25:08
  SPEAKER_01: There's no certainty in how many hours you're gonna have to work, there's no certainty in how much you're gonna make from moment to moment.
• 25:15
  SPEAKER_01: And it literally feels like a gamble.
• 25:17
  SPEAKER_01: And when you make a lot, unexpectedly, there's a thrill, and maybe that keeps you going.
• 25:23
  SPEAKER_01: And when you make very little, you go home feeling desperate and devastated.
• 25:30
  SPEAKER_01: And workers often describe gig work as gambling for this very reason, and they also will say things like, the house always wins.
• 25:39
  SPEAKER_01: Again, sometimes you you think you're coming out ahead, and and then you subtract your expenses, you subtract insurance costs, you subtract gas costs, you subtract vehicle depreciation.
• 25:50
  SPEAKER_01: So you get into an accident, and there's not the same kinds of commercial insurance coverage for this work as there should be or has been historically.
• 25:57
  SPEAKER_01: And so then all of a sudden you're out of work and you have no un there's no unemployment insurance.
• 26:02
  SPEAKER_01: And so they've said, you know, the house always wins, just like it does in a gambling house.
• 26:06
  SPEAKER_01: You might think you're coming away with a little, especially if you're a casual gambler.
• 26:11
  SPEAKER_01: But if you do this full-time and this is your source of income, then this is this is not the kind of work that provides security and stability.
• 26:26
  SPEAKER_00: So these companies say that their algorithms are just matching supply and demand.
• 26:31
  SPEAKER_00: What is wrong with that?
• 26:32
  SPEAKER_00: And why does it keep working as a defense?
• 26:34
  SPEAKER_01: That's such a good question.
• 26:35
  SPEAKER_01: So one one I would say that they're not matching supply and demand.
• 26:39
  SPEAKER_01: And we've seen this through all kinds of experimentation.
• 26:42
  SPEAKER_01: If I am driving for Uber and Lyft and you are a passenger and you're sitting right next to me, likely you're not gonna, I'm not gonna get the job.
• 26:52
  SPEAKER_01: Um, and we don't know why, uh, but probably this has to do with how they're controlling me and how much how many wages they want me to earn and um maybe how close I am to getting my bonus.
• 27:05
  SPEAKER_01: But it's not just matching supply and demand.
• 27:08
  SPEAKER_01: Uh, it is fundamentally both creating a labor market, creating a ride heel market, or a food delivery market, and and manipulating both of those things through algorithmic decision making in ways that ensures that you pay as much as you might be willing to pay, and I earn as little as I might be willing to earn.
• 27:30
  SPEAKER_01: And so at the very premise that they are just matching people is uh fundamentally you know oversimplified and maybe even incorrect.
• 27:45
  SPEAKER_01: But it's not so much that it's bad that they are creating these services or that they're businesses trying to earn profits, it's that they're low-road employers, they are exploiting people, they they meet the definition of exploitation to do what they're doing.
• 28:06
  SPEAKER_01: You know, um, plantation owners used to say all the time in the in the antebellum south, like, oh, well, we're providing for our slaves, we're giving them a place to live, we're giving them food, we're giving them purpose, and these are all things that we should be applauded for.
• 28:23
  SPEAKER_01: But the reality was that those workers were working under conditions of unfreedom and inability to imagine their own lives in in ways that we want all humans to be able to imagine them and to live them.
• 28:36
  SPEAKER_01: I might say the same things about low low-road employers like gig companies who who are creating working conditions that fundamentally endanger their workers occupationally, so physically, people who do this work because they're working so hard and so long for so little are more uh experience more injury than in other sectors.
• 28:59
  SPEAKER_01: They are unprotected, they're not getting health insurance for through their work, they're not getting workers' compensation in the way that workers employees are owed workers' compensation, and they are working 80 hours a week and unable to make ends meet.
• 29:16
  SPEAKER_01: Like that is not freedom.
• 29:17
  SPEAKER_01: That is not freedom of, there's no freedom of contract there, there's no there's no freedom to to live and work in the ways that offers dignity and um in and real independence.
• 29:29
  SPEAKER_01: And again, it's not just that they're that they're matching supply and demand, which you know I would question, it's it's the way that they're doing it, it's the working conditions that that they're creating.
• 29:42
  SPEAKER_00: It feels terribly unethical, but if a listener opens the DoorDash app tonight to pick up a shift, what is happening psychologically that keeps workers accepting these offers, even bad ones?
• 29:56
  SPEAKER_01: Yeah.
• 29:56
  SPEAKER_01: So most of the folks who do this work are people of color and immigrants.
• 30:02
  SPEAKER_01: They are people who are excluded from other kinds of jobs, they're marginal to the labor market.
• 30:11
  SPEAKER_01: And so many people feel that they don't have another choice, which is you know, might be actually the case, that there's not a lot of other choices here, which is why we want to ensure like this is precisely the kind of work that employment laws were passed to protect.
• 30:28
  SPEAKER_01: People who have very little labor market power should have some basic state protections.
• 30:33
  SPEAKER_01: And so, um, you know, if someone opens the app today and um feels desperate to work maybe casually because their first job is not paying them enough, the psychological drive is like this sucks, this is awful, but I have to do it because otherwise I'm not gonna be able to pay my rent, I'm gonna be evicted.
• 30:57
  SPEAKER_01: This is terrible, terrible work, but I don't have any other option.
• 31:01
  SPEAKER_01: If I go, I like maybe I try to get a work at McDonald's job at McDonald's, but McDonald's wouldn't give me regular shifts, so I have to do this.
• 31:10
  SPEAKER_01: And so, like when you go to work, when you when you get in your car or on your bike and start working for these companies, it's you're already often in a place of desperation.
• 31:22
  SPEAKER_01: And and you're just hoping, and that's again, you know, goes back to this feeling that this is gambling.
• 31:28
  SPEAKER_01: You're just hoping that maybe today you're not gonna get injured.
• 31:33
  SPEAKER_01: You're hoping that maybe today you're gonna get a good, uh, a good fare.
• 31:38
  SPEAKER_01: You're hoping that maybe you only have to work three hours and not ten.
• 31:41
  SPEAKER_01: You're making all of these kinds of of calculations and a lot of it, a lot of it rooted in things that are outside of your control.
• 31:51
  SPEAKER_01: And so oftentimes workers will say that every few days they'll have a really good day.
• 31:56
  SPEAKER_01: And I can't help but think that's like part of this, that it's that's like intentional, that the idea being that you had one really good day, and that keeps you going, that maybe the next day will be really good, maybe the next day will be really good.
• 32:11
  SPEAKER_01: And if there is something intentional to that, then it's quite insidious.
• 32:14
  SPEAKER_01: It's a way to combine insights about human psychology with labor control through machine learning systems that the the insidiousness of it is so really dystopian.
• 32:30
  SPEAKER_00: So now to focus more on the solutions.
• 32:32
  SPEAKER_00: For a student who wants to work on these problems, law, policy, research, what's one concrete thing they can do right now to start finding their way in?
• 32:43
  SPEAKER_01: That's a great question.
• 32:44
  SPEAKER_01: Um, so you know, I think that law school is a great avenue for a lot of this.
• 32:51
  SPEAKER_01: Law school is very expensive, but I don't think that should necessarily deter people at the outset.
• 32:56
  SPEAKER_01: There are a lot of scholarships, a lot of financial aid that you can apply for and and potentially get.
• 33:03
  SPEAKER_01: So I just want to put out there that you shouldn't write off law school because of the expense that you that you anticipate that there are um that there are ways to to fund your way through law school, including I, for example, TA'd a lot of my way through law school and fellowships and so didn't come out with debt.
• 33:22
  SPEAKER_01: I think that one thing that they can concretely do is start reading about this stuff and so that they really understand.
• 33:31
  SPEAKER_01: And I remember being in college and graduate school, just like going to the library and reading the New York Times, you know, in in uh in paper form.
• 33:41
  SPEAKER_01: But one thing, one kind of place that I would encourage folks to go is to Labor Notes, which is a wonderful resource for kind of news around what's going on with law and organizing in the country.
• 33:55
  SPEAKER_01: I would listen to some podcasts.
• 33:57
  SPEAKER_01: Um, one podcast that Labor Notes has their own podcasts, which is wonderful.
• 34:02
  SPEAKER_01: One podcast that I listen to a lot is The Dig, which is like a very kind of academic, esoteric podcast, but a lot of really good information around political economy and capitalism and what's happening.
• 34:15
  SPEAKER_01: Um podcasts like this.
• 34:18
  SPEAKER_01: But I would say go look for information about things that you uh care about.
• 34:22
  SPEAKER_01: And sometimes it's an uphill battle, like you have to learn some basic things before you understand what people are talking about.
• 34:30
  SPEAKER_01: But read books too, like they're all kind of look for syllabi online on labor law and political economy, and start reading the things in the syllabus, just like you know, um, engage uh engage your curiosity in a um in a way that helps you feel like you can be a part of these worlds and movements.
• 34:51
  SPEAKER_00: So you're saying read, learn about these different issues.
• 34:56
  SPEAKER_00: Are there any specific skills that you think will matter most for our next generation working at the intersection of labor and technology?
• 35:06
  SPEAKER_01: A skill that I see really lacking and one that I'm worried will deteriorate further because of machine learning systems is writing.
• 35:14
  SPEAKER_01: And so writing is not just like output, right?
• 35:17
  SPEAKER_01: 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.
• 35:25
  SPEAKER_01: 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.
• 35:37
  SPEAKER_01: And so I think one skill that I think is just so, so critical that I'm very concerned people don't have the desire to cultivate is writing and rewriting.
• 35:49
  SPEAKER_00: I was always told that reading, writing, and public speaking are some of the most important skills to learn.
• 35:58
  SPEAKER_00: Over the next decade, how do you think AI will be changing careers today?
• 36:03
  SPEAKER_01: Yeah, I think initially we're going to see a lot of labor displacement.
• 36:08
  SPEAKER_01: We're already seeing computer scientists not getting hired right out of college.
• 36:12
  SPEAKER_01: But the reality is that in a few years, these same companies are going to realize that if they don't train computer scientists, then they're not going to have the next generation of computer scientists.
• 36:22
  SPEAKER_01: I think that there will be an initial kind of labor displacement moment in some fields.
• 36:28
  SPEAKER_01: Maybe in, for example, in the fields that I am, you know, for lawyers, I think the same thing is going to happen that firms are not going to hire as many first and second year associates because they can rely on machine learning systems to do doc review or to write basic memos, but then they're going to realize, oh, we actually need lawyers for the more sophisticated lawyering, and we can't have those lawyers unless we train those lawyers.
• 36:52
  SPEAKER_01: So I my prediction is that there'll be a hiring uh drop, and then that there'll be kind of a shift, an adjustment in a lot of these different jobs that we're worried about at the moment.
• 37:05
  SPEAKER_00: And on a final thought, do you think algorithmic wage discrimination will eventually be illegal or just become more common and sophisticated?
• 37:15
  SPEAKER_01: Currently it's getting more common and sophisticated.
• 37:17
  SPEAKER_01: So it's spreading to the healthcare sector, to customer service, to retail, to education.
• 37:24
  SPEAKER_01: Very terrifying, I think, move on the part of these firms across different more traditional sectors of employment.
• 37:32
  SPEAKER_01: But I think that I see a lot of state-level efforts to regulate this practice because there is some bipartisan support around the idea that if you work really hard, you should be able to earn enough to support yourself.
• 37:47
  SPEAKER_01: This is like a basic idea of fairness under capitalism.
• 37:50
  SPEAKER_01: And so I remain hopeful that this will properly be regulated, that it will be banned in some states and maybe over time you know, banned as a practice.
• 38:02
  SPEAKER_00: Well, Professor DuBall, thank you so much for joining us today on Paths of Curiosity in an AI world and for sharing your perspective on how AI is reshaping the world of gig work and the lives of the workers behind it.
• 38:17
  SPEAKER_01: Thank you so much.
• 38:18
  SPEAKER_01: It was just so nice to chat with you.
• 38:25
  SPEAKER_00: This has been Paths of Curiosity in an AI World.
• 38:29
  SPEAKER_00: Explore careers in the lands of AI.
• 38:32
  SPEAKER_00: Until then, curious.