Artificial Intelligence (AI)

  • Artificial Intelligence (AI)
    The Year of AI and Elections
    Podcast
    Billions of people will take to the polls next year, marking the world’s largest-ever electoral field. But this historic scale is not the only thing that will make 2024 unique. As new threats like deep fakes become cheaper and more widespread, these upcoming elections could serve as a test run for democracy in the artificial intelligence (AI) era. What risks does AI pose to elections next year? And will a surge in AI-powered disinformation change the nature of democratic elections?
  • Labor and Employment
    U.S. Strikes and Global Trends in Labor and Productivity
    Play
    A. Michael Spence, distinguished visiting fellow at CFR, provides a global perspective on the changing landscape of labor and economic productivity. Sharon Block, professor of practice and executive director of the Center for Labor and a Just Economy at Harvard Law School, discusses this year’s strikes and the economic implications of increased collective labor activity in the United States. A question-and-answer session follows their opening remarks. TRANSCRIPT FASKIANOS: I’m Irina Faskianos, vice president for the National Program and Outreach here at CFR.  We’re delighted to have over two hundred participants from forty-seven states and U.S. territories with us today. CFR is an independent and nonpartisan membership organization, think tank, and publisher focused on U.S. foreign policy. CFR is also the publisher of Foreign Affairs magazine. And, as always, CFR takes institutional positions on matters of policy. Through our State and Local Officials Initiative, CFR serves as a resource on international issues affecting the priorities and agendas of state and local governments by providing analysis on a wide range of policy topics. So, again, thank you all for joining us today. As a reminder, this webinar is on the record. The video and transcript will be posted on our website after the fact, at CFR.org. And we will circulate it to you as well. We are pleased to have Sharon Block and Michael Spence with us to talk about U.S. Strikes and Global Trends in Labor and Productivity. I will give a few highlights from their bios. Sharon Block is a professor of practice and executive director of the Center for Labor and a Just Economy at Harvard Law School. Recently, she served as a senior official in the Office of Information and Regulatory Affairs in the Biden administration. From 2017 to 2021, Professor Block led the Labor and Work Life Program at Harvard Law School, where she focused on labor law reforms to build a more equitable economy. Prior to that, she’s held various senior positions in government, including principal deputy assistant secretary for policy and senior counselor to the secretary of labor. Michael Spence is a distinguished visiting fellow at the Council on Foreign Relations and clinical professor of economics at Bocconi University in Milan. He is also a senior fellow at Stanford University’s Hoover Institution and the author of the book, The Next Convergence: The Future of Economic Growth in a Multispeed World. And in 2001, Dr. Spence was a co-recipient of the Nobel Prize in Economic Sciences. So thank you both for being with us. Sharon, I thought we could begin with you to give us a sense—give an overview of the increased collective labor activity in the U.S. that we’ve seen this year. If you could discuss the different strikes and the common threads, AI among them.  BLOCK: Yeah, happy to. And thank you for having me. It is—I’m sure it will be a really interesting conversation. So just set the stage, this summer into fall, I think, was a season like no other in recent years in the U.S. labor movement. There were approximately half a million workers who went out on strike in 2023. And a lot of that activity, again, was concentrated in the later part of the year. Another way of thinking about that is more than four million workdays were spent on strike instead of working. And to put it in context, that’s double the number of workers who went out on strike in the U.S. in 2022. So really a big upswing. But, to sort of pull back and put it in even sort of longer historical context, it’s a much, much lower number than what we saw decades ago in the sort of the high point of union density in the United States. You had millions and millions of workers out on strike, and a much greater part—share of the economy would be affected by those strikes. But in terms of, like, say, the last twenty to thirty years, this was a very significant year.  The biggest strike in the United States this year was the SAG-AFTRA strike. That’s 150,000 union members were on strike. That’s a little bit of a funny number because SAG-AFTRA members are obviously a little unusual. They don’t go to work—typically they don’t go to work every day. But they are a very big group that withheld their labor, along with the Writers Guild. So you had Hollywood shut down for a significant period of time. The next biggest strike this year was the strike at Kaiser Health Care. Those are mostly SEIU members. That was about 75,000 workers who were out for three days. And then the strike that got certainly the most attention and was, I think, the third-biggest strike this year, was the United Auto Workers and their sort of novel strike strategy vis-à-vis the big three auto companies. Now, they did not take all of their members out at one time. So that was about 50,000 workers. There are more members in the UAW than that. But it was still a very significant number of workers, even with this sort of staggered strategy.  So in addition to those three very large strikes we also saw strikes in the hospitality sector, in Las Vegas at the casinos, also L.A. hotels, and then in higher education. So the most of these strikes were really centered in the private sector. But we did have University of California, graduate student workers went out on strike. That was a very large strike. And then Rutgers University faculty and staff went out on strike. Now I would add to this an almost-strike, if you really want to think about how dramatic this activity was in the United States. The UPS workers—the Teamsters at UPS didn’t actually go out on strike, but took a strike vote, came very—like, within hours of going out on strike, at which time they were able to reach an agreement with the company. But it’s a similar dynamic of the threat of a strike that led to that agreement. But say the theme among many of these strikes was that they existed—they happened between bargaining partners who have a very mature collective bargaining relationship. You think about the auto workers who have been unionized at the same three companies—you know, one of the companies has changed their name—but essentially organized at those three companies for almost a hundred years. These are not the kinds of bargaining relationships that have dominated sort of labor news over the past year or two, like Starbucks and Amazon, where you have new collective bargaining relationships. We didn’t see strikes among those workers. We saw them in these very established relationships. The other theme among these strikes, really almost universally, were very, very big wins for workers. They settled these strikes with agreements that, I think, were objectively viewed as very advantageous for workers. You saw very high levels of public support for the workers in almost all of these strikes.  And then, to your point about AI, these are also strikes that happened, for the most part, in sectors that are in big transition. In some, because of the introduction of AI. That was obviously a very big theme, a big factor in the Hollywood strikes, but also other transitions. In the autoworkers strike you had the issue related to transition to an EV future played a big role, in healthcare that’s obviously an evolving field. So this idea of there being a big transition and workers using their power through their strike in order to get contracts that help them have more of a say in the future. And then I would say one last theme that was very prevalent in many of the strikes was the sort of rhetorical and motivating theme of workers wanting to have their fair share. You heard that phrase come up a lot. So we’re talking about sectors where the companies had had a recent history of very high profits, workers who were locked into collective bargaining agreements that they had negotiated sort of before the pandemic. So if you think about, like, UPS had very, very high profits during the pandemic. The Teamsters were working under a contract that didn’t anticipate that level of profits. You have—the auto companies were also coming off a couple of years of very high profits. And so you have this theme of workers really wanting to get their fair share of this increased revenue and profits that they saw coming into their—into their companies. The last thing I will say is just if you want to understand just sort of how positive this strike season was for workers, you just have to look at the UAW contracts. I mean, there are so many things about this strike that were just groundbreaking, or at least groundbreaking as of the past few decades. You saw wage increases of 25 percent for permanent workers and 150 percent for some of the temporary workers. You had really novel provisions in the collective bargaining agreement that they eventually signed to keep open or reopen auto plants. We’ve never really seen that before in a collective bargaining agreement. And workers preserving the right to strike over any other plant closures. As I said, you got this foothold in the EV future in agreements for the companies to recognize the union in these EV battery plants. And so, it was just a really remarkably positive contract that ended the strike in the auto sector, really transforming the UAW to be able to say, again, that a job in the auto sector equals a good middle-class job. And we’re seeing now the autoworkers taking that message to the nonunionized companies—Tesla and the transplant companies—to say, look what we got for workers at the big three. Wouldn’t you like to have this too? And you’re seeing actually these companies already responding by raising wages. So it’s also a strike that has had pretty significant ripple effects already. One thing to watch in 2024 is how far those ripple effects go, how successful they are. Will this season of successful strikes for workers actually lead other workers to want to organize a union in their own companies, in their own sectors, maybe even beyond the auto sector? And, again, we did have some groundbreaking provisions that came out of these strikes around AI. The Writers Guild, most significantly. You had agreements that AI can’t be used to undermine the writer’s credit, requirements for studios to disclose if they’re giving any material to writers that was generated by AI. But then also, in a sort of more positive embrace of AI, the right for writers to choose to use AI as a tool as part of an agreement with the studios. SAG-AFTRA, the actors also got provisions sort of protecting their images from AI replication without their consent. And the Las Vegas—the hospitality unions also got provisions guaranteeing them advanced warning of any new technology rollouts that were going to impact jobs and training for jobs that are altered by AI. And, really importantly, protections from certain types of AI that enables surveillance within the workplace, something that was very important to hotel workers who have been increasingly surveilled in their work. So there is a lot to dig into. I’m going to stop talking so we can get to some questions, because there’s really—could go on and on because it was such a fascinating period of time. FASKIANOS: Fantastic. Thank you so much, Sharon.  Michael, let’s go to you to pull out a little bit and talk about the global trends you’re seeing, and the implications for the future workforce and labor movements. And you just recently authored an article in our magazine, Foreign Affairs, The Coming AI Economic Revolution, with James Manyika. So perhaps you could talk a little bit too about the AI piece of this as well. SPENCE: Well, thank you very much. And I’m, you know, like Sharon, very pleased to be with you. So let me approach these things, you know, at a sort of slightly different level. That three-decade period that Sharon referred to is a period in which a massive amount of productive capacity was introduced into the global economy, largely as a result of emerging economy growth. And that had one very large negative effect, which was it, you know, created options for, you know, labor arbitrage and decreased the power of American labor. So unions declined, you know, the middle class got hollowed out to some extent, and so on. That force is fading. It’s not over, but it’s fading. There’s lots of evidence of that. You know, for at least two decades, probably more than that, we lost employment in the manufacturing sector. That stopped in the last decade. And then—but then there’s some other trends that, you know, kind of reinforce this. So when I look, you know, I see aging populations. Seventy-five percent of the global GDP is produced in in countries that are aging rapidly. You know, the great financial crisis caused some of our older fellow citizens, like me—not to retire. Now they’re retiring in droves. You know, when I look at the American economy all—most of the big labor, you know, employment sectors have labor shortages, right? I mean, it’s clear that on the underlying economic fundamentals, labor’s power position vis-à-vis their employers has increased dramatically. Some of this shows up in unionization. Some of it just shows up and in bidding for, you know, talent in a way that basically companies didn’t have to before—or, employers in general. So I think this is basically a good development. I expect to see, you know, several attractive trends. A reversal, maybe not a dramatic one, in the trends in inequality on the income side, which would be very good thing because it had gotten pretty extreme over this three-decade period. You know, I think we will see productivity increases because when you’re short of labor it’s sort of natural to start looking—the incentives are much stronger to look for productivity-enhancing things. And if that’s done in a way that makes—you know, puts management and labor in a collaborative position, seeking for ways that are mutually beneficial to do it, that’s also a good thing. On the negative side, you know, this is—you know, for the first time, really, we live in a supply-constrained world. I just—you know, at the risk of telling people what they already know, after the great financial crisis we’ve had—and for a longer period than that—we’ve had essentially no sign of inflation whatsoever. And we had no sign of inflation, in spite of zero interest rates and massive infusions of liquidity into the economy to try to precipitate a recovery after the balance sheet damage that the great financial crisis caused. And as a result of that, people have kind of gotten used to the notion that, you know, the cost of capital isn’t very high. So for people who are operating in state and municipal governments, I think, you know, there’s—nobody knows for sure. And we have a big inflation fight on, led by the central banks. Not just in the United States but in the U.K., and in Europe, and in other places. China being a fairly dramatic exception to this. We’re likely, in my view, to emerge from this with higher real interest rates. I don’t have any doubt that the central banks will get the inflation eventually under control, because they’re determined to do it and their credibility depends on it. That’s their job. But when we come out, I think we’re going to have, you know, lower sustainable debt levels, higher cost of capital, lower multiples, lower valuations for many assets. This will have mixed effects. You know, the cost of funding, certain longer-term investments is going to be a little bit higher than it was before, maybe even more than a little bit higher. On the other hand, from a distributional point of view, you know, when—in the period—the decade after the great financial crisis, the one thing that just ballooned in value was the assets. And that favored people who, you know, own a lot of assets. So it didn’t do wonders for the distributional features. So I think on the whole, if you sort of look at—I mean, there’s a lot—a lot of other factors, you know, that are affecting this. The global supply chains are, you know, collapsing—or, being fragmented. We have a major strategic competition, you know, with China underway. Economic policy, from an international point of view, has tipped toward, you know, various kinds of security—national security prominently, but also economic security, here in Europe energy security, food security, and so on. And this is causing, you know, policy to reinforce a trend in the global economy that’s very visible now, which is diversification in pursuit of resilience.  And the policy is reinforcing it and saying: We have to do some of this at home in a way that we didn’t pay attention to before. We lived for three decades, those three decades, in which the way global—the global economy was constructed was basically on the basis of economic efficiency and comparative advantage. And that’s no longer true. So we have homeshoring, friendshoring, nearshoring, et cetera. All of which are transforming the structure of the global economy. And for the most part, I think, in ways that favor, you know, domestic—our domestic fellow citizens, and especially labor. Briefly on AI. So, we’ve had a sequence of breakthroughs in AI that go back, you know, a decade or a bit more. Language recognition. You know, image recognition was a stunning set of breakthroughs that, you know, occurred roughly around 2015-2016. But the one that’s really gotten people’s attention is generative AI, the large language models and the like. So there’s several things to say about this. And I’ll try to be brief. One, we’re not at the end of this. These folks aren’t finished. So what’s coming next we don’t know. I suspect that we will see significant advances in robotics as a result of the fact that gen AI allows you to basically talk to machines in a way that they understand.  The gen AI is distinctive in the sense—in two respects that I think are important. One, unlike any other previous version of AI, they switch domains easily. By that, I mean, you know, you can talk to it about the Italian Renaissance and then switch to math and then it’ll do computer coding, you know, and whatever, right? Now, there’s lots of quirks, you know. These systems so far have hallucinations. They make stuff up. And I mention that for a reason. You know, it’s not—when you look at it carefully, it’s not sensible to think that these things should be fully, you know, allowed to operate on their own, right? They’re just not that flawless. You know, there’s a famous story in America, you know, a lawyer, slightly incautiously, prepared a legal brief entirely using ChatGPT, and handed it in. Well, ChatGPT made up all the legal precedents. And this gentleman is, I think, in some serious trouble as a result with the courts.  So the way I think about it, and I’m not alone in this. I mean, James and I wrote that paper. We think that the right model is powerful digital assistant or machine-human collaboration, right? And you have to work that out. But let me say, you know, right at the top, there’s just overwhelming evidence that whenever you mention, you know, AI, people think, automation. They think they’re coming for our jobs. A hospital administrator stands up and starts talking about AI—and, by the way, AI is going to be transformative in biomedical and life sciences, which is not our topic for today. But it’s just one of the many places where the footprint over time will be felt. We have to overcome this bias. So the implementation matters.  You know, unions representing people and having a voice in which they participate in conversations about what the AI is supposed to be doing and how it will change the jobs, and which parts are acceptable or not. But I think in the course of it we can sort of get rid of this—what I call the automation bias. Erik Brynjolfsson at Stanford calls this the Turing trap. Alan Turing proposed that we evaluate our progress in AI by asking the question: Can we produce a machine that when a human being interacts with that machine, not looking at it but talking with it, it thinks it’s interacting with another human being? And so we haven’t got there yet, but we’re working on it. Second, one small step. Almost all AIs are benchmarked against human performance. So when they declare victory in image recognition, it’s when it passes the average human, and so on. It’s the next small step that’s dangerous, which is, you know, well, once the machine passes the human, why don’t we replace the human, right? That’s where the AI—the automation bias comes from. And it’s just a mistake. Now, there may be a time in the future when these machines are so good that automation is a more serious consideration. But right now, they’re powerful digital assistants. They can sometimes do things that humans can’t do. Sometimes they do them, you know, in a way that’s just on par. But they—but I think the promise here is if we do this right that we’ll have the potential—not next year, not the year after, but maybe by the end of the decade—we’ll start to see, you know, impacts of this and in terms of productivity that are actually, you know, enhancements to the way people work and how they view their employment. To get there, we got to get rid of the automation bias, which is very deep. And we need one other thing. We need access. So right now, we’re in a period of intense exploration and experimentation. Who’s doing this, right? The answer is the companies with the resources to do it, you know. But if we’re going to have this broadly beneficial in society, available to small businesses, to local governments, and so on, it has to diffuse widely and well beyond, you know, the kind of entities that have the capacity to invest tens of millions of dollars in it. There’s a role for government in this.  And I want to conclude with this, because we’re talking to, you know, important government officials in our economy. There really is a role, you know, in ensuring diffusion and broad-based access to these tools once we’ve decided, you know, in rough and ready terms, you know, how we’re going to try to use them. It’s really important, both for the purpose of getting the productivity surge but also, you know, for preventing—you know, in past—there’s studies of this at the McKinsey Global Institute. In past, you know, episodes of digital, you know, transformation, they’ve studied adoption. So and what you see is a pattern of divergence. So the tech sectors way is way up top, and finance is not far behind, and then you drop down and find sectors that, you know, are lagging seriously in this respect. This is the pattern that we do not want to repeat on this round.  So I think there’s huge potential. There’s some downside risks. James and I would say that, you know, it’s important to pay attention to the misuse of these things and the downside risks, just as there is with any powerful technology. I mean, gene editing is terrifying if used in the wrong way, just as AI is as well. But there’s the positive agenda as well. FASKIANOS: Thank you both very much. And now we’re going to go to all of you for your questions. We’ve got the first written question from Riley Nye: Hasn’t automation already replaced tons of manufacturing jobs? SPENCE: Sharon, do you want to take that, or? BLOCK: I mean, certainly there has been displacement. Earlier this year I visited a Ford auto plant. And it is very, very—a very, very different place than certainly historical documentation of what the manufacturing process looked like. There are a lot fewer workers. You see that also reflected in—just to stay with the auto sector—in the number of UAW members in that sector. The UAW has actually diversified their membership a great deal. They do a lot—as many people on the call may know if you’re involved with higher education—they represent now many, many graduate students and other employees of higher education institutions. So, yes, that has happened. But the displacement conversation is obviously not over. And there is, I think, concern about additional displacement of workers if robotics and those kinds of productivity enhancements, or whatever the right euphemism is, continue. But I do think in the shorter term, the bigger concern actually should be for workers is the way that automation is being used in workplaces to enhance not just productivity but also employer domination of workers. These surveillance issues you’re seeing, especially if you follow, like, the logistics sector. The intensification of work that is enabled by the kind of productivity tracking that AI has enabled. I think these are the changes in the workplace that people are feeling already, even before we get to this question of whether AI is ever going to—or when it’s going to be good enough to replace more workers. And so that’s a place where I think the regulatory attention really needs to be paid. And if you look at what the EU just did—we don’t know the details yet of the AI Act that the EU Parliament just passed—but there is a lot of attention to those issues. And, in fact, the workplace is designated in that legislation is a high-risk forum for the introduction of AI, not because of the displacement issues but because of the intrusion into sort of personal privacy spheres for working people, and this potential for new safety and health issues to arise from a misuse of AI in the workplace. FASKIANOS: Thank you. I’m going to go next to Justin Freeman, who is the director of community affairs for New York State Assembly: Could you share more about how today’s strike actions compare to before, say, thirty, forty years ago? You mentioned four million strike days. BLOCK: Yeah. So, again, we haven’t had a year with four million strike—it’s actually more than four million at this point. That doesn’t capture the full range of the UAW strike days. But I just couldn’t find a more recent calculation. That’s a little bit of a hard calculation to do. We haven’t seen a four million strike day year in a long time. So, say, ten to fifteen years ago. But if you look at, like, in the 1930s, so the period as we were legislating the right to join unions, you would have—there were years in the 1930s, early 1940s, where you had thousands of strikes in the United States.  And, you know, it’s hard to compare numbers because our economy is obviously so much bigger now. Our workforce is so much bigger. But if you could imagine thousands of strikes. It was really a completely different scale. I mean, if I could show you a graph from, like, the ’40s to today, you would see a line that just really dramatically falls off as we entered this period, you know, that we’ve been talking about, like the past thirty years of a real decrease in the strength of the labor movement. You saw a commensurate decrease in the number of strikes. FASKIANOS: OK, thank you. Shawn has asked a question. China was mentioned. How big of a threat do you feel China is, with their housing, population, and debt crisis? SPENCE: OK. So I think everybody knows that China, you know, has had kind of a pretty impressive forty-year run. It was one of the poorest countries in the world in 1980. It has exhibited growth rates of, you know, 7, 8, 9 percent on a sustained basis that, you know, causes, you know, the size of the economy and incomes to double faster than every decade. That’s not something an advanced country can do. I think China is now in a very difficult sort of position in terms of transformation. And the economy is in trouble.  So they have major, major excess capacity in real estate, and a lot of non-performing loans, and whatnot. This directly affects the Chinese economy because while this is, to some extent, true in other places, the household balance sheet—meaning wealth—is heavily dependent on real estate, right? Once they started buying houses and so on, they just owned more real estate and less kind of other kinds of assets than almost anywhere else in the world. And so when the real estate values decline, or there’s some uncertainty, or it gets shaky, or, you know, the apartment that you bought in advance doesn’t get built, it causes a major shakeup in confidence.  The fiscal system needs major reform in China. The municipal governments are essentially flat-broke. They do not have the normal sources of revenue that our municipal governments do. And they are responsible for delivering services that are in excess of their capacity to finance it. There’s no more land. They used to do it by selling land. There’s no more land to sell, or not enough to finance themselves. But I think the really sort of serious challenge, in addition, in China is that the pattern of off again/on again and fairly aggressive regulation, which you can see in the tech sector but it’s broader than that, has caused a loss of confidence among investors.  And by that, I don’t mean foreign investors. I mean, everybody, including the domestic investors. And so with the household spending, you know, a little bit on hold, and the private sector investment, you know, kind of on hold because they’re not sure what their place in the sun is, that, you know, there’s a significant slowdown. The numbers in China will look OK because the previous year was a disaster. So when you’d show up growth numbers, you know, when they were in zero-COVID, it was, you know, unimpressive. So the numbers look better than the actual situation. Having said that, you know, it is in many ways—you know, in human capital, in science and technology, and whatnot—they’ve made huge investments in it. So I don’t want to leave the impression that this is a kind of, you know, permanent disaster at all. They can pull this out. And it’s an economy—it’s a very large economy, the second-largest one in the world. And it will be, if they right the ship on these—what I think of as short- and medium-run challenges—it’s a, depending on how you think of it, a powerhouse and potential major competitor. FASKIANOS: And, Michael, just to follow up on that, a question from Alan Schneider, who is legislative director in the Office of Maryland Delegate Chao Wu: You know, given the relationship changes between the U.S. and China, how are the changes affecting wage, A1, and inflation here in the United States? SPENCE: Very good question. So in terms of, you know, the—we are in—our national security, you know, driven policies are bringing more stuff home. And in addition, China is now an economy with a per capita income of $13,000. You’re not going to make, you know, the cheapest labor-intensive, process-oriented manufacturing and assembly stuff in China for very much longer. There is no real substitute for China. There are other countries. And some of them are benefiting—Vietnam, Bangladesh, you know, Mexico has a major opportunity as China sort of, A, gets into kind of conflict with us and, B, you know, we move both businesses and governments in behind with policy to move stuff away. I think on the whole it’s slightly inflationary. But it’s good for, you know, labor—meaning, our labor. FASKIANOS: Terrific. Let’s go to—sorry. Going to sell in Selin Zorer: What proactive steps should federal and state legislators take to ensure that the emergence of AI benefits the public? SPENCE: Sharon, do you want—do you want— BLOCK: I can—I can start. I mean, you know, I think one way to ensure that it benefits public—I mean, most of the public has to go to work every day. And so thinking about the ways to protect workers from some of these abuses and excesses is really important. The other—I think the other area where I’m really interested is while we see some paralysis at the federal level in terms of legislating around the introduction of AI into the workplace, I think there is much more of an opportunity for state and local governments to step in. California is obviously very engaged in their legislature in thinking about guardrails for AI in the workplace and in other domains.  But it’s really important that as this regulatory forum moves to state and local levels, that there is an attention to making sure that the people who are going to be most affected, that working people have an opportunity to have a voice in how this regulation develops. And so whether that’s bringing in the labor movement, finding other ways to ensure that working people are participating in these really important conversations, I think is going to be critical. And I hope we’ll see sort of interesting and innovative approaches as more states feel compelled to get into the game. Because we are probably not going to see, you know, significant federal regulation or legislation in this space. FASKIANOS: Thank you. Next— SPENCE: Irina, could I— FASKIANOS: Go ahead, Michael, absolutely. SPENCE: This is—I don’t want to repeat myself but, you know, there’s a positive agenda. You know, a lot of the, you know, management that affects people’s lives is done at the state and local level. Not, you know, the kind of stratosphere where some parts of the federal government operate. And, you know, I think, you know, thinking carefully about where we’re going with these technologies and how you help people, you know, become comfortable with them, productive with them, and so on, is a hugely important part of the agenda. And I can’t think of more important entities than the state and local governments, you know, the community colleges. You know, the education system as a whole seems to me to be, you know, where the conversation needs—you know, a fair amount of the conversation needs to occur. So, again, I don’t want to, you know, minimize the importance of preventing downside risks and misuse and so on. But I think walking into the world, you know, without a coherent set of programs to help people—you know, if we are going to have these transformations in one form or another. That it’s way too powerful, these technologies. So I think the challenge is to do it right, rather than resist them. FASKIANOS: Fantastic. I’m going to go next to Justin Freeman, director of community affairs at the New York State Assembly: Is there any correlation between interest rates and strike actions? Can strikes be anticipated through economic indicators? SPENCE: Go ahead. FASKIANOS: You can take it. Who wants to start? BLOCK: I think Michael mentioned the most important factor, which is a tight labor market. I mean, that is clearly connected to this upsurge in labor activity, both in the strike activity and then also in this renewed commitment to organizing. There’s just—there’s a lot of risk under our legal system. There’s a lot of risk to workers who try to organize a union, who go out on strike. We have a law that’s really deficient in terms of protecting workers who engage in that kind of labor activity. And so a tight labor market gives workers the confidence that if they are retaliated against for taking this kind of activity, that they can find other jobs. And it’s really as simple as that as to why you see that correlation between a tight labor market and increased union activity. So I think that’s the most important factor. I think the issue of interest rates is just whether the Fed was going to raise interest rates enough to start driving that unemployment rate up and creating slack in the labor market, which then would have taken some of this dynamic—diluted this dynamic so that workers didn’t have that same confidence in their ability to find other jobs when they take the risk of organizing or striking. FASKIANOS: Michael. SPENCE: Yeah, I mean, essentially the same. I mean, so we—you know, the supply side of our economy, and the global economy has just changed dramatically, right? So it used to be almost infinitely elastic. You could have a surge in demand and, you know, somewhere somebody produced enough to meet it. That’s just not true anymore. That’s why we have labor shortages, as Sharon says. That’s why labor power is increasing. And as for inflation, you know, the trigger, you know, as we came out of the pandemic with a predictable surge in demand, and the supply side constrained by, you know, aging—you know, all the things we talked about. You know, we had a demand and supply imbalance. It was the trigger for inflation. Now, inflation can develop a life of its own, you know, once it goes on for long enough. But, you know, the economists look at this and say: When have we seen interest rates go up this fast and this high and not seen, you know, labor market problem? We just—you can go back a long way and try to find out an example of this. So, you know, this—what this tells you is that, you know, we have fundamental structural changes underway in the economy. And they—and the relationship between the labor markets and the inflation is that, you know, it triggered the inflation because the supply side couldn’t keep up. Now, what’s going on now, and I’ll just end with this, is, you know, the central banks, you know, can’t operate on the supply side of the economy. So they’re basically raising interest rates largely to reduce aggregate demand and get rid of that imbalance. And so far, they managed to do it without, you know, producing unemployment increases of any significant magnitude because there are labor shortages—short version.  FASKIANOS: Great. Thank you. I’m going to go next to Aaron Tebrinke, who’s legislative assistant to Leader Koehler of the Illinois Senate: After 148-day strike, Hollywood screenwriters secured significant guardrails against the use of AI in one of the first major labor battles over generative AI in the workplace. A battle for automation against AI, automation was won by labor. But what protections will workers have to keep up with AI tools in the marketplace that are not regulated for privacy? SPENCE: Sorry, can I—the concern of the writers was that, you know, they were going to get displaced, you know, by the use of, you know, the kind of generative AI, the large language models. That was, like, I don’t—that’s a bit of automation, but the underlying concern was copyright, right? Which is a major issue, right? Because it—you know, gen AI is trained on the entire internet. They just go read everything, you know, at speeds that exceed human capacities. So the question is, well, what’s the relationship between that and all the imaginative content that these and other people have produced that the AIs just hoover up? So that strike had multiple dimensions to it, and not all of them had to do with automation, for sure. FASKIANOS: Sharon, anything to add? BLOCK: Yeah, I think that that’s right. Obviously, we’re seeing litigation by content creators, many of whom are members of the Writers Guild, in order to get at this issue of their intellectual property rights vis-à-vis the use of these—the use of that content by the large language models. So I think we are going to continue to see many different fronts in the introduction of AI into the workplace, and as it impacts workers in different ways. So but to just to answer the, the part of the question about privacy, we have a very, very weak privacy regime vis-à-vis the workplace in the United States. And so you really—in the private sector. Now, that’s different in the public sector because you have a constitutional dimension to privacy in the workplace with public sector employers. So some of this might sound—might sound different than your own—than your experience, since folks on this call are from the public sector.  But in the private sector, we don’t really have an institution of privacy protections—as we now have AI surveillance of things like, you know, your email. There are employers now who very easily can just scrape every email that you write to find out all kinds of things about you, and you probably don’t even know it. That can watch you through the camera on your laptop when you’re working from your home. So I think these privacy concerns aren’t new in the workplace. But I think they’re going to be appreciated by, I hope, policymakers, but also by workers in a new way, as we see different uses of AI in the workplace. FASKIANOS: Great, thank you. I’m going to go next to Nate Belcher, who is a fiscal analyst for Arizona Joint Legislative Budget Committee: UAW included a thirty-two-hour workweek with no pay reduction as one of their bargaining points in their recent negotiations. Do you think that reducing the length of the workweek will become a more popular demand from labor in the coming years? BLOCK: Yes. I think—I mean, I think we’re seeing it already. I mean, I would say just a few years ago there was almost no serious discussion of a four-day workweek. That is now an issue that is on the table. I don’t know of any workers who have secured a four-day workweek through collective bargaining. There are certainly employers who, of their own volition, are experimenting with shorter workweeks, sometimes with four-day workweeks. You know, I don’t think that many people thought that the UAW contract at the end of the day would actually include a thirty-two-hour workweek.  I think it was put on the table as just another way to discuss hours. I mean, what was really an issue in the UAW strike I think around hours was the fact that many, many workers were being forced to work a lot of overtime. And even if they were getting paid for that overtime, it was having such an impact on their quality of life that it was really an entree to talk about what it is like to have those kinds of time demands, and what workers want in terms of having some kind of balance in their lives to be able to do with their time what they want. But I think the thirty-two-hour workweek is a conversation we’re going to continue to see bubbling up. FASKIANOS: Thank you.  Next question from Paul Egnatuk, who is the legislative aide in the office of the Michigan State Representative Jim Haadsma: I’ve heard recently of brick-and-mortar type investments stalled because investors are enamored with AI ventures. Can you recommend sources of research on the impact of private capital going toward AI development and/or where capital may be short for other pressing needs? SPENCE: Right, this is complicated. I mean, so, you know, there’s a massive amount of money going into AI. So some of the valuations are probably a little bit off the chart and, you know, that’ll get corrected over time. Some of us will remember the internet bubble, which had some of the similar characteristics. But that doesn’t mean there’s nothing there. But, you know, if you look at, I mean, vis-à-vis the previous subject, you know, hybrid working is becoming a very prominent feature of a subset of the economy where you can do that, right? And, you know, if you go into New York now and go into an office on Friday—you know, you’re very likely not to find anybody there. I mean, my friends tell me, don’t even bother. You know, so that doesn’t mean the work week is shorter, but it means, you know, that there’s substantial changes in the real estate sector and, you know, excess capacity of one kind, people—economic activity is moving around. I mean, on the whole, I would say the investment situation in the United States is reasonably healthy. You know, for the first time we have sort of major investments in infrastructure, you know, that have been funded by the government. And the CHIPS and Science Act has some more major investments, some of it designed to bring activity at home. And then we have the Inflation Reduction Act, which is designed to put, you know, funds into the energy transition in pursuit of sustainability. So when I look at the whole—I mean, there’s imbalances all over the place because of these structural transformations. And I’m sure we could find places where there’s significant deficits. But on the whole, I think the investment program, you know, or the investment situation looks moderately healthy. You are going to see just huge investments in the digital technology side as people pursue this set of opportunities. FASKIANOS: Sharon. BLOCK: Yeah, I don’t think I have anything to add. I mean, it is—it does feel like we are seeing more manufacturing jobs. I think we’re all—having come out of the Biden administration, I’m really excited to see sort of the full implementation of the Inflation Reduction Act and the CHIPS and Science Act. We just—I think, last week the president visited a site of the first—like, one of the first major investments. So I think that might balance out, you know, the kinds of trends that the questioner was raising. FASKIANOS: Emily Walker, who’s legislative director for Pennsylvania Senator Katie Muth, asked: Can you talk a little bit about the wave of organizing that’s been taking place in southern United States recently? BLOCK: Yeah, happy to. It’s a very interesting dynamic. So a couple of different trends. There has been a concerted effort, particularly driven by SEIU, Service Employees International Union, to do some kind of innovative organizing in the South. You know, the South is a very challenging place for the labor movement. Has been for a long time. And so there’s been a push to not do traditional union organizing but just try to get as many workers engaged in collective activity without necessarily using the traditional model of an NLRB election for majority exclusive representation within their workforce. You know, the South now is pretty much universally right to work, which just makes it a very challenging environment for traditional union organizing. So I think we’re going to continue to see these kinds of innovative campaigns. They’re really more like campaigns than organizing drives. The counter to that, though, is, like in the Starbucks organizing, there’ve been about more than 300 Starbucks stores that have unionized, and a number of those are in southern states. The South has not been able to sort of put up that wall to union organizing, at least among the Starbucks organizing, that they have in terms of a lot of other sectors. But the other dynamic, which it’s too early to know whether it’s going to be successful or not, but is what I raised at the outset about the UAW’s intent now to organize the transplant car companies. Almost—not all of which, but which predominantly have located their manufacturing in the South. And we also have—Ford is building the Blue Oval Plant, which is going to be, I think, one of the largest auto manufacturing plants in the country, if not the world. And they have now made a commitment to not try to stop the union from coming into Blue Oval. So that’s in Mississippi. That is going to be a union plant. That’s a big deal. But then the big question is going to be whether the UAW can organize other car companies that are not union in this. And I’ll note, they’re not union in this country. Most of these companies have unionized workers everywhere else in the world. And they seem to figure out a way to make money in plants in other countries with unionized workforces. They come here, and they fight the UAW sort of tooth and nail to keep the union out of their plants here, again, which are mostly located in the South. So, you know, we’ll see. I think after this most recent UAW strike, underestimating the new president Shawn Fain is not a good idea. He did things in this strike that nobody thought he would be able to pull off. So I think, again, one of the big stories in 2024 is going to be whether we’re going to see inroads for labor in the South, particularly through these auto companies. FASKIANOS: Thank you. And I’m going to sneak in one last question from Charisse Childers, director for Arkansas Division of Workforce Services: Michael stated it is not possible to think that robots can operate on their own. Do we have employment data on jobs that were added solely in conjunction with added technology? In the same vein, jobs lost solely in conjunction with technology, meaning robots? SPENCE: So, I mean, this a little bit nerdy, but, I mean, robot—human beings, you know, especially people who actually make things and, you know, do things in a physical environment, have, you know, an extraordinary capacity that robots don’t have. Which is an ability to absorb a rapidly evolving, you know, external environment, you know, visual and other signals, essentially with no latency. Robots aren’t even remotely close to that. And if you want evidence of it, look at the, you know, challenges facing the autonomous vehicles. You know, they do fine in highly structured environments, you know, where, you know, you’ve painted all the lines on the road, or you’re in a parking lot, or something like that. And then you put them in sort of an unusual situation, and they drive into a pile of cement or, you know, the emergency responders don’t know how to deal with them, and so on. You know, in other words, in unstructured environments, you know, the robots basically need help navigating around, even if they have the mobility and, you know, manual dexterity, and other things that are other dimensions of robotics. There’s people working on this problem, but I think, you know, this is an example—you know, one of the many—in which I think robotics and people are going to work together. You know, and you’re not going to see full automation. Maybe in structured environments. I mean, you see some of it—you look at—it’s not just manufacturing. You look at a, you know, major distribution center, an Amazon distribution center, there’s—you know, there’s a lot of robots. And this isn’t very snazzy technology. They just don’t bump into each other and they go collect things and bring them to the people who pick and pack them, scan them, and so on. So, you know, there’ll be progress in this. But my—having spent some time talking with AI people, I think that, you know, full automation, except in highly structured environments, is a fairly long way away. And we’re going to see mostly human—you know, human-machine kind of collaboration and those environments. And there are a lot of them. I mean, you know, if you go outside distribution centers and manufacturing things, and highly structured, you know, roadways and whatnot, pretty much everything else is unstructured, right? Hospitals, et cetera, so. FASKIANOS: Thank you. Well, unfortunately, we are out of time. But this was a terrific discussion. So thank you, Sharon Block and Michael Spence. We appreciate it. And to all of you, for your questions. We will be sending a link to the webinar recording and transcript, as well as some of the other resources that were mentioned. You can follow Dr. Spence’s work on CFR.org and Professor Block on X, formerly known as Twitter, at @SharBlock.  And, as always, we encourage you to visit CFR.org, ForeignAffairs.com, and ThinkGlobalHealth.org for the latest developments and analysis on international trends and how they are affecting the United States. Of course, please do share your suggestions with us for future webinars and any ideas on how we can help you in the work that you are doing in your communities. You can email [email protected]. We wish you all a happy holiday season. And we look forward to reconvening this series in fiscal year—or, actually 2024, which is right around the corner. So, again, thank you both. We really appreciate it. SPENCE: Thank you. Thank you. (END)
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    Jocelyn Benson, Michigan secretary of state, and Marc Rotenberg, executive director and founder of the Center for AI and Digital Policy, discuss how officials can prepare for challenges posed by AI in U.S. elections. A question-and-answer session follow their opening remarks. TRANSCRIPT FASKIANOS: Thank you. Welcome to the Council on Foreign Relations State and Local Officials Webinar. I’m Irina Faskianos, vice president of the National Program and Outreach at CFR.  CFR is an independent and nonpartisan membership organization, think tank, and publisher focused on U.S. foreign policy. We’re also the publisher of Foreign Affairs magazine. And as always, CFR takes no institutional positions on matters of policy. Through our State and Local Officials Initiative, CFR serves as a resource on international issues affecting the priorities and agendas of state and local governments by providing analysis on a wide range of policy topics. We’re delighted to have close to five hundred participants from fifty-one states and U.S. territories for today’s conversation, which is on the record. And we will share the video and transcript after the fact at CFR.org. We are pleased to have Secretary Jocelyn Benson and Professor Marc Rotenberg with us today to talk about “Elections in the AI Era.” And Secretary Benson is joining us from a car. She’s very busy, so we are happy—thank you very much for doing this. She is currently serving as Michigan’s forty-third secretary of state. She has received multiple national awards for her work to ensure the security and fairness of Michigan’s 2020 and 2022 general elections. Secretary Benson recently launched Michigan’s Truth Tellers Task Force, comprised of community leaders who speak with voters about their election and misinformation concerns, build trust before and after elections, and provide transparency in the electoral process. Professor Marc Rotenberg is an adjunct professor of privacy law at Georgetown University, and the founder and executive director of the Center for AI and Digital Policy. He has served as an expert advisor on artificial intelligence to many international panels, organizations, and Congress. And in 2018, he helped draft the Universal Guidelines for Artificial Intelligence, a widely endorsed human-rights framework for the regulation of artificial intelligence. So thank you both for being with us. Secretary Benson, I will begin with you, if you could tell us about the threats you see as you look ahead to the 2024 elections and what steps you are taking and you would share with other election officials to ensure that elections are secure and accessible, especially given these new challenges posed by AI. BENSON: Yes. Thank you for having me. And it’s a critical discussion on a number of fronts. I would say, you know, since the 2020 election, the—American voters and election administrators American democracy writ large has been through a lot. And we’ve also learned a lot, as well, about who our adversaries are, what their tactics are or will be, and what their goals are. And so we’re going to leverage a lot of that intelligence, that information to prepare for on all fronts to protect and secure our elections in 2024. But there are three sort of emerging issues that are new on our table that we are particularly concerned about. One, of course, are emerging technologies, which I know we’re going to talk about—artificial intelligence, the newness of it, the new frontier, and all of the panoply of possibilities it creates for election malfeasance, and interference, and confusion. Second is the additional collapse, I guess you could say, of social media and other ways of getting information or spreading misinformation, which has opened new doors and more doors than ever before for the spread of the misinformation that particularly is enabled by the new and emerging technologies that heretofore—up to this point are highly unregulated in terms of their usage. And then the third piece—and this is, to me, the most important element of this—is that our adversaries—the adversaries to democracy, I would say—have more of an incentive than ever before—than they did in 2016 and 2020 and 2022—to actually interfere with our elections, because the outcome of the presidential election in America in 2024 will have a direct impact on wars against democracy overseas, particularly in Ukraine. And so Russia, Iran, China have a greater incentive than ever before to try to influence our elections process. So new or perhaps more highly incentivized adversaries; new and emerging technologies; and a collapse of trusted sources of information, and—i.e., social media in particular, have set us up to have a real challenge in 2024. But the good news is we’re ready. We’ve been through this in many ways in 2020, overseeing in Michigan the highest-turnout election and a successful, secure election in the midst of a pandemic. So we have the tools and resources and the brainpower and grit, frankly, to overcome these new and emerging issues that are going to plague our elections in ’24, but it’s not going to be easy. We have to be preparing and planning now with all partners a whole-of-society approach, from academia to the federal government, executive branches in every—to local governments in every state, even candidates and voters who need to be empowered and prepared to ensure that the, let’s—for lack of better words, weapons of choice for our adversaries to interfere with our elections, be it technology, AI, misinformation writ large, are all totally unsuccessful. And so I’m happy to talk about what we’re doing on that front to ensure they’re not successful, but I’ll—and I’ll emphasize, too—I’m sure we’ll go into greater depths on that in our discussion, but I’ll just emphasize two things at the out front. One, we know the goals of adversaries to democracy, and particularly American democracy, are three things: to create confusion—confuse voters, cause them to disengage; to create chaos—chaos that would, similarly, cause citizens to say I want to give up on democracy altogether, it’s not working; and to create fear—if I vote, if I participate, something bad might happen. So chaos, confusion, and fear are the goals. So our response needs to be rooted in giving the American citizens, no matter who they vote for, confidence, clarity, and certainty that democracy will work, and our elections will be secure, and that their vote will count. And we have to back that up with real action at the federal and state level on all fronts to recognize that in everything we do we have to give voters confidence; we have to create clarity as to what happens when you vote, how to trust you vote and all the rest; and ensure certainty on the outcomes and on the procedures in voting as well. The last thing I’ll mention on all those things is that the messengers—developing stronger, trusted messengers of the truthful information about our elections—how to vote, how to trust our results, how to respond to misinformation—that’s going to be crucial as we enter into this election season. And that’s a role that everyone can play on various forms. And so I’m sure we’ll talk a little bit more about that, but proactively educating voters through trusted messages is our best antidote to misinformation that will flow into our communities in the months ahead, and I know you all have—doing that. FASKIANOS: Thank you very much. That was great. Marc, over to you to give your perspective. ROTENBERG: Well, thank you very much, Irina. Nice to be with you, Secretary. I think this is a great topic that we’re discussing today. I was listening to Senator Schumer earlier. He was describing the AI agenda in the U.S. Senate and the various bills that have been introduced, but he said the one issue that we need to prioritize now concerns AI and election security because 2024 is such an important year—which is true, by the way, not only in the United States, but also around the world. You have elections in Mexico, in the EU, and India, and elsewhere. So people are very focused on this issue. I also want to say that me and my team at the Center for AI and Digital Policy have been working with several international organizations over the last few years to help develop frameworks for the governance of AI. And the phrase that you see reappear in many of these governance frameworks is trustworthy. People want to ensure that there are standards and norms established to ensure trustworthy AI. But with the emergence of generative AI, which is essentially an elaborate statistical technique for inferring outcomes based on large datasets, if I might put it that way, we’re also creating new tools that we don’t fully understand even in the creation of the voice and the—and the video and the text. Campaigns are now experimenting with these tools, but they’re not entirely sure what the consequences will be. And I think that should give us pause. It actually reminds me a bit of the early days when people were talking about online voting. Online voting continues to raise, of course, a lot of concerns for American voters about security and reliability, and we need that careful technical assessment to determine how best to manage some of these techniques that are now being deployed. I think the secretary makes an important point also when we think about these new techniques and some of the vulnerabilities it’s not simply in the context of trying to influence others as to how someone may vote. As a general matter, we don’t, you know, oppose the use of radio and television or the internet to get political views out, but it’s an entirely different matter when you’re dealing with foreign adversaries whose goal may simply be to disrupt an election, to reduce public trust, to create outcomes where democratic states are less confident in their own governments. And so I think we need to be looking at this challenge through that lens as well. Now, we have sent statements and recommendations to the—to the Senate Rules Committee, to Chairman Klobuchar and Ranking Member Fischer. We’ve made comments recently to the FEC regarding a proposed rule to extend some of the limitations on campaign advertising to include, for example, the deceptive use of generative AI techniques, because it is remarkably easy now with some of these tools to have your political opponent speak words that, in fact, she never said. And this can be done quickly, it can be done at scale, it can even be personalized. I’ve been reading, for example, about one of the key techniques with chatbots is the ability to engage in a profile-based dialogue where you know something about the individual, you engage with them online, and you continue a conversation with the aim of trying to persuade them as to an outcome. Now, some, you know, experts in the campaign field will say, well, of course, you know, over many years campaigns have learned to target certain groups based on certain interests, so of course that’s a familiar strategy. But what I don’t think people fully appreciate is that’s essentially a once-in-time communication, whether it arrives as an email, or a text message, or a pamphlet under the door. What ChatGPT makes possible is actually the ongoing engagement with the voter, with the aim of trying to persuade and doing so in a way that we’ve never seen before in elections. So I think state election officials are going to have a lot of challenges ahead as they try to assess these new techniques. One of the themes that we see in many of the governance frameworks for artificial intelligence is a strong emphasis on transparency. People should know when they’re interacting with an AI tool. They should know the source of the message so that they can verify it if they need be. And I would even, you know, propose a bit of a warning to the campaigns that are experimenting with these techniques. I would encourage you to really make sure you fully assess the outputs that the techniques are generating because we have seen in the last several months, of course, many of the leading experts in computer science working closely with generative AI techniques have actually been surprised—surprised by the results that they can produce that weren’t predicted and maybe in some instances a bit troubling. I will say also that we raised some of these issues earlier this year in a complaint that our organization filed with the U.S. Federal Trade Commission regarding OpenAI’s product ChatGPT. We pointed to some of the risks regarding the use of ChatGPT in elections. I’ve done similar complaints to the FTC in the past, but what was striking about this particular complaint is that OpenAI itself, in its technical self-assessment known as the system card, actually describes the risks—the risks of using ChatGPT for influence operations, not only the profiling and targeting but the ability to use the technique to disseminate misinformation. And so I think we’re in a moment, as I said, where we’re going to face new challenges. I think the work of election officials is about to enter a new phase. And the phrase I hear oftentimes from the experts in the field is that what we’re seeing is a rapid acceleration at scale, so the conversation we are having today may not even be the conversation we’re going to be having in spring of 2024 as we get closer to the elections. I would recommend, as I said earlier, more transparency, more accountability, notification as appropriate, and also of course training election officials to put in place the necessary safeguards and to identify the risks that could arise from the use of AI techniques for misinformation and disinformation. Trust is absolutely central to the democratic process, and we need the ability to ensure that the outcomes that are produced through our elections are outcomes the public accepts and respects. It’s not at all clear in this—at this moment that generative AI is going to take us closer to that goal, and I think that is the challenge. FASKIANOS: Thank you so much. So we’re going to go to all of you now for questions and comments. So you can raise your hand, you can write your question in the box, and I will read it. Secretary Benson has to depart in ten minutes or so, so let’s prioritize raised hands for questions for her and then we’ll go to Professor Rotenberg as well. But while we’re waiting for questions to queue up, Secretary Benson, you talked a little—you said you would talk in the discussion about what—the steps you’re taking to protect elections. Maybe you could share some of the things you’re putting in place, especially given what Professor Rotenberg has said. BENSON: Yes. I think the protections—and we need legislative changes in order to do a lot of them. But the protections, there’s a few that are legislative and one that is administrative. On the legislative front, you know, we recognize that the federal government is proposing legislation to protect elections from deceptive AI. We’re working with and to look at what states like Minnesota and others have done to develop two bills in Michigan or two policies in Michigan, one that will require instant disclosure or disclaimers anytime any type of communication involves AI, period, whether it’s deceptively used or not. And so disclosures and disclaimers is one. And then criminal penalties for when there is AI utilized in an effort to deceive, particularly to intentionally deceive voters about an election policy, or how to vote, or when to vote. You know, if we see—or whether an election is canceled, or election-related information. So criminalizing or providing criminal penalties for individuals who intentionally use AI to deceive voters about their rights, about the actions they can take to vote, and then also getting legislation that would require disclaimers and—of any use of AI communication on any political communication as even just issue discussions. So those two pieces of legislation have already been introduced. They’re working their way through the legislature. And other states are following suit. Minnesota has also just passed or was considering some legislation. And of course, the Rules Committee has also had a hearing in the U.S. Senate on similar legislation. The third thing we need to do is prepare voters to know that this is coming. And while we typically are educating voters about the whens and the hows and the—and the whats to actually cast your ballot—where to get your ballot, where to return your ballot, what early voting is, how to register to vote—we now have to add a component to all of our voter-education pieces through all of the voices—the myriad of voices from faith leaders to community leaders to sports leaders to educational leaders about AI and its potential harmful effects on democracy. We have to empower and equip voters with the information they need ahead of time so that when AI does land—and we have to anticipate it will—that we can—that they’re equipped and empowered to know what it is and to not trust it, in a way, and to be critical consumers of information in this election cycle. So I think those three things will put us—along with working with our partners in the federal government to ensure that we’re partnering with CISA and the other agencies for any foreign interference and all of that, and consequences there—will also continue to be at the forefront. And the last thing I’ll emphasize on that is that we—you know, federal law and the laws coming out of Congress would apply specifically to federal candidates, not to state candidates, not to local candidates. And so just because the federal government is acting or does act on this front, it doesn’t—every state should still be looking at it. It doesn’t, you know, absolve the states for the role and even local governments in the role we need to play in enacting disclosure/disclaimer regulations, as well as criminal penalties for those who would seek to deceive voters about their rights. FASKIANOS: OK. Thank you. I’m going to go take the first question from Councilmember John Jaszewski, who has raised his hand, if you can accept the unmute prompt. Q: Is that it? Did I get it? FASKIANOS: Yes. Q: OK. My question is simple. You know, the bad actors speak the loudest. They have the loudest megaphones and therefore seem to dominate sometimes the discussion. Are there any specific things that local officials like myself, small communities, can do to, you know, overpower that bad actor, that loud voice from the bad actors? BENSON: Yes, there are. In fact, I would argue you have perhaps one of the most critical roles to play, because you are as a local official closer to the ground, real—there in real-time, and a trusted voice in your community. So, one, I would say preemptive or proactively preparing your constituents to know this is coming, to know what they can do to report it, provide a way that they can report it. We have in Michigan a portal to report any type of misinformation, including AI-driven misinformation, which is an email and a portal on our website that people can use to report it so that we know about it and we can respond to it as well and debunk it. But you are a connector to the citizens who are targeted by deceptive AI. And you can use that connection and your trusted voice to them to help them be prepared now for what’s coming. Have a town hall talking about this. Invite other experts to talk about what AI is going to mean for every voter, how to intercept AI on social media, how to push for the state and federal changes we need to require disclaimers, and disclosures, and criminal penalties. And then, you know, be there throughout the cycle to identify AI, report it, call it out, and equip voters to do the same so that we proactively are able to be ready for when those hits come. And through education and empowerment, and clear to-dos of what to do when it when it hits, have everyone part of intercepting AI before it can have its intended impact of deceiving voters and causing chaos and confusion around elections. FASKIANOS: Thank you. I’m going to take the next question from David Burnett. Q: Thank you. We were talking about deceptive practices with AI. We’ve already seen an example where one candidate for president used an AI-generated voice to verbalize a statement of his opponent, which the opponent had made but in written form only. So that’s a bit of a nuance of whether or not that’s actually deceptive to use for audio ads, something that the candidate did say but not verbally. BENSON: I think that’s why it speaks to the need to have a disclaimer and disclosure for the use of any AI, so the people know that artificial intelligence has been used to make this commercial. That is our first step in equipping voters. Not getting involved in the intent or deception sort of piece when AI is used, and giving voters that basic information. And certainly, the criminal penalties involved in intentionally using it can come later, or as the process plays out through, you know, accusations of intent and all the rest. But I would argue the example you gave should have a disclaimer on it for voters to know at the very least that this was AI-generated audio. FASKIANOS: Thank you. Next, I’m going to take a written question from County Commissioner Nikki Koons: Can either speaker go more in-depth on how AI would be able to be used in an election process? I live in a very rural area and am not hooked up to the internet during voting. Are we just talking about AI generating any type of information prior to the election? BENSON: I think I mean—go ahead. ROTENBERG: Well, I was just going to say that even if you don’t have internet-based voting, you likely do have internet-connected voters who are receiving advertising and communications online. And, of course, campaign ads through internet websites are one of the most popular ways today in the United States to reach voters. And I think you should anticipate that those online campaign ads will reflect some of these new generative AI techniques. And, again, it will sort of maximize the opportunity for misinformation that’s also highly targeted, because you take the profile of voters that you know and you’re able to extend that over a period of time, which was not something that was possible in the past. FASKIANOS: Secretary Benson? BENSON: Yes. Yeah, so I certainly think and we’re anticipating the use of AI to be part of what interferes with election processes as well. This could include the creation and spread of localized misinformation on election day, putting out information saying there’s—you know, falsified—making falsified claims conditions at the polls, even perhaps suggesting violence in certain precincts as a way of deterring—you know, falsely claiming that—as a way of deterring people from showing up to vote. Knowing that there are a handful of states, and in particular you could argue in Wisconsin and others even just a handful of precincts, that could directly influence the outcome of the presidential election.  There’s a way to potentially target different areas with that misinformation to drive voters away and to dissuade voters from showing up to vote at all with misinformation around wait times, closures of polling places, and other types of things. You should see it as a potential voter suppression tactic that can be easily deployed on election day. That is where it’s most likely to interfere with the operations of elections itself, and why we need to, in some cases, have an operation in place to rapidly respond to information that gets out there.  And we already do that in Michigan in general, because misinformation can be generated by humans on social media. And so we already have a rapid response network in place to identify, source, and respond to misinformation about polls closing or violence at the polls. And so we have to expand that in that regard. But we certainly have to anticipate that the use of AI could, even if it’s not—and this has not really been the tactic of foreign adversaries anyway, to actually interfere with the hardware of elections. It could interfere with the people that make elections, both voters and election administrators, come to fruition. And that’s going to be their most likely target. And it’s going to be a target that is about creating that chaos and confusion and fear among voters, and even among election workers who may fear threats that could interfere with their ability to oversee a presidential election. FASKIANOS: Thank you. I’m going to go next to Lanette Frazier. Q: Thank you so much. I would like to know, as a city councilperson, what—is there already some kind of verbiage out there that we can use as far as language to create a disclosure disclaimer scenario, and also some kind of legislation to criminalize AI that we can use instead of starting from scratch? BENSON: Yes, I’m happy to—I’m happy to share with you the information of the bills we’ve introduced in Michigan. There’s federal legislation as well that has language that we borrowed from. And Minnesota and a few other states have already proposed or enacted legislation. So there are sample bills out there. And perhaps—I’m happy to get them to you in terms of what we’re doing in Michigan through CFR. And I hope, you know, organizations like NCSL and others that typically compile these model laws and policies will also include this in their portfolio. But in the meantime, we’re happy to get you the language that we’re using in Michigan. FASKIANOS: Fantastic. And we can share that with the whole group, as well as a contact at NCSL with whom we work. I’m going to go next to Christian Amez. Q: All right, so just kind of piggybacking on that previous question but a little bit more detail. You know, what kind of, you know, criminal penalties are—you know, should be included in state legislation to kind of prevent people from using generative AI, deep fakes, et cetera during these elections? Because, you know, one thing that I’m worried about is that the sort of penalties are so low or just become the cost of doing business when it comes to running elections and campaigns. You know, how do we—what’s the bar that we should set to make sure that it becomes prohibitive to use this sort of technology in a misleading way? BENSON: Think if you have identified—if anyone has identified that bar, let us know. I mean, we’re in a new frontier here. You know, certainly, this is a new, emerging, advancing technology that is advancing so quickly. As was said earlier, what even what we know is possible now could change in six months. And the collection of ways in which bad actors can take advantage of this new and emerging technology, particularly in the time where they have such a high incentive to do so, is in many ways limitless. So we know that disclosure of, you know, particularly AI-generated deep fakes and other content that could mislead voters is one of our—you know, disclaimers on that information is one of the ways that we can equip voters with the information they’re going to need to know what to trust and what not to trust. And then certainly penalties for intent to deceive are going to be evolving as the attempts to deceive evolve as well.  So I think the most important thing in this moment is that we prioritize to collectively solve this problem, which we’re starting to do. And I think Senator Schumer’s comments particularly are really well taken and appreciated. And it seems to be a bipartisan prioritization. And then to create, as we’re doing in Michigan, expert-driven workgroups to help us track the evolving and emerging threats as the technology evolves and emerges, knowing that, again, our information, and even the use of the technology is going to be very different, likely, one year from now when the election is happening than it is even right now. So we have to build up an infrastructure to adjust and adapt as well and evolve and develop new solutions as we go along. FASKIANOS: Fantastic. And just to say, Lanette was from Arkansas and our last question came from New York. I’m going to go next to a written question from Mike Zoril, who’s a supervisor in Rock County, Wisconsin: Can AI algorithms be developed to both detect voter fraud in real-time by analyzing patterns in voting data, and also allow citizens to verify that their vote was counted accurately? Marc, I don’t know if you can speak to that.  ROTENBERG: Right. So that is actually a very interesting field right now, particularly with regard to generative AI and establishing techniques for watermarking. There were at the White House now I think fifteen companies that have pledged to incorporate watermarking in the creation of generative AI output, which includes the text and the audio. I’m not quite sure how it’s going to work with audio, but also video. And that can reduce the likelihood of using generative AI without detection. But, of course, there will always be countermeasures. And people will try to evade detection on try to evade some of the compliance requirements. There is a phrase I’d like to just share with everybody that that we have used repeatedly to try to help people understand what the unique challenge is with generative AI in the election context and other contexts as well. We say that generative AI can both mimic and manipulate human behavior. Which is different, I would say, from how we’ve oftentimes thought of computer systems and simple issues, let’s say, of security or accurate vote tabulation. Because with a generative AI, you’re now producing a text, a voice message, that sounds familiar and sounds convincing. And once that conversation and point of contact is created, then there’s the opportunity for persuasion. And that’s what I think election officials need to be on the lookout for. FASKIANOS: Thank you. OK, so with that I’m going to—we’re going to keep going with Professor Rotenberg, but Secretary Benson does have to go. She’s on her way to an appointment. So I want to thank you, Secretary Benson, for your time with us today, for all that you have done in defending democracy for the country. And we will circulate the resources that you mentioned to the group. So, again, thank you for being with us today. And we really appreciate you wedging this into your very busy schedule.  BENSON: Thank you, all. Thank you. It’s been wonderful. Thank you for this discussion. It’s so important. And I look forward to more discussions in the future. Thank you. FASKIANOS: Thank you. All right, Marc, we are going to continue on with you. And I’m going to next go to—let me see—John Bouvier. And if you could identify yourself, that would be great. Q: Yes, thank you. My name is John Bouvier. I’m a councilman with the town of Southampton. And my question—I’m an engineer, so forgive me for thinking in the other direction. But I see that AI potentially has a use in voter verification and particularly in the technology of collecting votes. And it seems to be at the discretion of a lot of different boards of elections across the country, on who they hire, what equipment they use. I’m wondering how that’s—how we protect against that in the sense that AI could be—could be a useful tool in that respect. But how do you guard against its misuse, particularly when it’s being used in equipment and computer systems that are identifying voters’ signature recognition, all that kind of thing? And it’s my push to standardize, I think, to make a standard on how that’s done, because it seems to be at such local discretion that the voter is sort of left in a state of mistrust as a result of things like AI as well. ROTENBERG: John, it’s a great question. I think it actually takes us back to some of the foundational issues around election integrity and the importance, for example, of using paper ballots that can be properly tabulated, or retabulated if necessary. Many of the concerns with digital voting, I suspect, will be amplified with AI, because there’s more opportunity for manipulation and for mischief. So there is a lot to be said for voter verification techniques that rely on, you know, paper documents. Not to discourage some of the more innovative approaches I know states are taking, but to have that as a backup and a reliable source of voter identification I think is a good foundation. And I think it’s also key to how we think about the vote tabulation process itself. FASKIANOS: Great. Let’s go next to—I’m going to go to City Attorney Carrie Daggett’s question for the city of Fort Collins in Colorado: What kind of practical suggestions do you have for detecting, proving, and enforcing the requirements for disclosure and prohibiting the use of AI? ROTENBERG: Yeah, not a simple question you just asked. I think, you know, and I’m saying this only half-joking, because, you know, many of the leading computer scientists have actually said that we need a six-month pause on this technology precisely because they can’t fully audit and assess the outcomes that are being produced. So I think—and maybe the secretary has some further information on this or NCSL, which is a very good resource, can provide some technical support. I do think it will be helpful for state and local election officials to establish your online presence as a trusted source of information so that as these questions arise if you need to flag, for example, particular campaign communications, there’s a way to convey that information to the public from a familiar source and from a trusted source. Because, of course, part of manipulation also is the effort that people sometimes make to present trusted sources. FASKIANOS: OK. I’m going to go next to—we’re coming to the end of our time, and so many questions. But I’m going to go next to Tim Duey, who works in the office of Senator Kathleen Kauth in Nebraska. Let’s see: I’m glad we’re discussing the dangers of AI abuse in elections, because it does seem like a big problem. I did want to ask though, couldn’t this be a game-changer for candidates in local down-ballot races, where there’s one person who needs to engage with many thousands of voters, if AI isn’t used deceptively or in an unscrupulous manner? MR. ROTENBERG: I’m sorry. I didn’t hear the question. FASKIANOS: Could it be used productively for a candidate in a local down-ballot who might not have the resources to, you know, get the word out about their campaign? Could AI take the place of volunteers to communicate their platform to voters that they would have never been able to reach otherwise? ROTENBERG: Yes. So, you know, it’s a good exercise, actually, to go online and try ChatGPT, and say: Draft talking points for me as a candidate. I intend to emphasize these three issues and here are the constituencies I’m trying to persuade and see the output. I think you’ll be surprised and impressed. As a first draft, the conversational AI models actually produce very good text. But over time, you know, they need to be interrogated and the risk of misinformation needs to be addressed. FASKIANOS: Thank you. I’m going to take, I think, probably the final question from Evan Collier, a raised hand. Go ahead, you’re unmuted now. I think we’ll be able to hear you, hopefully. OK, we seem to be having technical difficulties. Maybe we can go next to Commissioner Kevin Boozel from Pennsylvania. Q: Can you hear me? FASKIANOS: Yes. Q: OK, perfect. You know, this has been a wonderful session, I got to say. My name is Kevin Boozel. I’m Butler County Commissioner in Pennsylvania. And I also sit as chairman of our County Commissioners Association. Elections have been extremely, extremely hostile in last couple of years. This is not going to help us. I appreciate the fact that the policies are coming from the federals. I would like to have those sent to us so that we can advocate for them for all of the counties. And, you know, this AI is beyond probably 90 percent of our brains. This is not something we deal with every day. But I think that the literature, educating the public of what to look for. You know, I’m on social media a lot. That’s about as far as I go. But a lot of the misinformation is on there, and then it’s removed. But it says: removed because inaccurate information, or something on the social media. I’m assuming that that’s because of bad actors. I don’t know that for sure. Some people get what’s called Facebook jail, they’re not allowed to post anything for a while. And this is all interactive. So how do you—how do you manage all these social media sites with one system, I guess is my question? And how do we hold people accountable? Because they created such a stir in our elections office in 2020. They over-sent out the applications to people, potentially from third parties. You know, it’s so hard to get a hold of this stuff. And I think AI is generating a lot of this mail in some fashion. I could be wrong. I don’t know. But this mail that people do. And maybe it’s just bad actors doing it by hand. I don’t know. But I just looking for policy. And I’m looking for the information that I can hand to people that are questioning what—the accuracy, again, and what they should do about it. How do they prove it, or how can they report it? ROTENBERG: Well, Commissioner, I have good news and I have bad news. I mean, the good news is I see that Andrew Morgan just posted in the chat the new report from NCSL on AI regulation in the state. So I’m sure that’s going to be a useful resource. But the bad news is that, you know, all of the major companies are dealing now with the—with the concerns around misinformation being amplified through generative AI. And the focus is, of course, the electoral process, because that will be the target for so many deployments over the next year. So you are literally on the front lines. I do think some of the challenges are going to be new and different. And you’ll need to communicate rapidly with your colleagues as these issues emerge. That’s certainly what we’ve experienced over the past year working on AI policy with the computer scientists. And they continue to be astounded. So I imagine there are a few surprises ahead. FASKIANOS: Marc, are there any closing thoughts you want to leave us with? ROTENBERG: Well, as I said at the outset, we’ve done a lot of work with international organizations on AI policy frameworks. And I don’t think there’s any doubt that ensuring trustworthy AI systems is the essential goal. The governance frameworks, the regulatory standards, the technical standards all need to be pursued with that as the—as the central mission. And, you know, it’s very encouraging, listening to the secretary and hearing these questions, the work that’s already underway. And I just, you know, wish you the best, because I know there will be some challenges ahead. FASKIANOS: Fantastic. So thank you, Professor Marc Rotenberg and Secretary Benson, who had to leave. Again, we will share this recording as well as the transcript with all of you, and some of the resources that were mentioned. You can follow Secretary Jocelyn Benson on X at @JocelynBenson, and Professor Marc Rotenberg at @MarcRotenberg. And, as always, we encourage you to visit CFR.org, ForeignAffairs.com, and ThinkGlobalHealth.org for the latest developments and analysis on international trends and how they’re affecting the United States. And, as always, we welcome your suggestions. You can email [email protected], as well as thoughts, feedback, and anything else. We always love hearing from you. And thanks for all the important work that you are doing in your communities. This is—it really takes everybody putting their hand in this to make sure that we safeguard our democracy. So thank you again to everybody. ROTENBERG: Thank you END