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- [00:01]
Lawfare Host
The following podcast contains advertising to access an ad free version of the Lawfare Podcast. Become a material supporter of lawfare@patreon.com lawfare that's patreon.com lawfair also check out Lawfare's other podcast offerings, Rational Security, Chatter, Lawfare, no Bull, and the Aftermath.
- [00:32]
Eugene Volokh
Listen up. You can get the new iPhone 16e with Apple Intelligence for just $49.99 when you switch to Boost Mobile. We pulled so many all nighters to give you the steal and hey, stop messing with the mic. I'm just helping this catch people's attention. This is a great deal. Exactly, so it doesn't need all that. Fine. Get the new iPhone 16e available at Apple Store locations and the Apple Store online. Visit your nearest Boot mobile store for full offer details. Apple Intelligence requires iOS 18.1 or later.
- [01:02]
Alan Rosenstein
Restrictions apply.
- [01:03]
Howie Mandel
There's a part of me that everyone sees. I'm Howie Mandel, the comedian. Apparently I know what funny is. Funny bought me a house. But I also know what isn't funny. Ocd. I've lived with OCD my entire life, and people throw the term around like it's no big deal. But OCD is severe, often debilitating. It's a mental health condition that involves unrelented unwanted thoughts that can make you question your character, your beliefs, even your safety. General therapy can help with some things, but for ocd, it can actually make things worse. That's why I want to tell you about nocd. NOCD is the world's largest treatment provider for OCD and is covered by insurance for over 155 million Americans. Their licensed therapists specialize in ERP, the most effective treatment for OCD. If you think you might be struggling with OCD, go to nocd.com to book a free 15 minute call. They are here to help.
- [02:05]
Alan Rosenstein
It's the Lawfare Podcast. I'm Alan Rosenstein, Associate professor of Law at the University of Minnesota and a Senior Editor and Research Director at lawfare. Today we're bringing you something a little different, an episode from our new podcast series, Scaling Laws. It's a creation of lawfare and the University of Texas School of Law, where we're tackling the most important AI and policy questions. From new legislation on Capitol Hill to the latest breakthroughs that are happening in the labs, we cut through the hype to get you up to speed on the rules, standards and ideas shaping the future of this pivotal technology. If you enjoy this episode, you can find and subscribe to Scaling Laws wherever you get your podcasts. And follow us on X and bluesky. Thanks for listening. When the AI overlords take over, what are you most excited about?
- [02:57]
Kevin Frazier
It's not crazy, it's just smart.
- [02:59]
Alan Rosenstein
I think just this year, in the first six months, there have been something like a thousand laws.
- [03:04]
Kevin Frazier
Who's actually building the scaffolding around how it's going to work, how everyday folks are going to use it?
- [03:09]
Alan Rosenstein
AI only works if society lets it work.
- [03:12]
Kevin Frazier
There are so many questions have to be figured out, and nobody came to my bonus class. Let's enforce the rules of the road welcome back to Scaling Laws, the podcast brought to you by lawfare and the University of Texas School of Law that explores the intersection of AI law and policy. Welcome back to another edition of the AI Summer School. I'm Kevin Frazier, the AI Innovation and Law Fellow at Texas Law and a contributing editor at lawfare. Today's class dives into one of the most complex and controversial aspects of AI and the law libel. Eugene Volek, a senior fellow at the Hoover Institution and longtime professor of law at ucla, is an expert in the field and penned a paper on the topic back in 2024. For those looking for extra credit, be sure to read in its entirety, including all appendices, large libel models, liability for AI output, and we've got a link in the show Notes for those content with a P or a passing grade. Our conversation is going to cover the essentials, so we've got you covered there. As always, we'll use our standard format here. First, we're going to explore the fundamentals of the law. In particular, we're going to dive into libel, and then we'll spend a bit of time looking at Section 230, the First Amendment, before Eugene will detail how AI maps onto these key aspects of the law. Finally, we'll discuss some open questions and let you get on with your day and hopefully get into our homework. All right, Eugene, thank you so much for joining the AI Summer School.
- [04:49]
Eugene Volokh
Thanks very much for having me. It's funny that you talked about my having penned a paper, even though I never used a pen and nobody reads it on paper. It's funny.
- [04:59]
Kevin Frazier
There we go.
- [05:00]
Eugene Volokh
And soon legacy media we've inherited through our language, even a computer. We talk about computers, but in most situations we're not using them primarily to compute, although of course a good deal of computation goes on in the background. There are data processors, their word processors, their communicators, their entertainment centers.
- [05:19]
Kevin Frazier
There we go. Well, I guess now too. My my lawfare colleague and co author Alan Rosenstein And I have talked a lot about how scholars will use generative AI to pen a paper. So what do we say then? Do we say we generated a paper?
- [05:34]
Eugene Volokh
Gend. Let's call it gend.
- [05:36]
Kevin Frazier
Gend. I like it. Okay, we're already creating new vocab. It's the sign of a great class. Jen, that's going to stick with me. All right, so we'll get our TM there on Jen. This is great. Well, Eugene, a key aspect of your paper is libel. And for folks who have forgotten their free speech course, or perhaps never took a free speech course and just skipped straight to the bar, what is libel? What are the key things we're looking for when we're talking about libel?
- [06:05]
Eugene Volokh
So to oversimplify, libel means false statements of fact about a person or a corporation for profit or nonprofit that damages that entities or person's reputation. And in order to prove up a libel case, you often have to show certain kinds of mental state. Famously, for example, if you're talking about public figures or public officials, you have to show so called actual malice or which is not actually malice, it means is knowing a reckless falsehood for speech about private figures. If you can show actual pretty provable loss as a result of damaged reputation, well, then negligence might be enough. So those are, generally speaking, the elements of libel. And of course, the libel could be by. It has to be in writing, generally speaking. But it could be. Could be handwritten, could be printed, and of course, it could equally be on a computer.
- [07:16]
Kevin Frazier
So it could be gend. We'll get to that in a second. We'll get to that in a second. But thinking also about libel, a couple key considerations come to mind that we'll map on later. But can you talk a little bit more about this publication requirement? Obviously, if I just whispered a libelous state or gen. A libelous statement and handed it to my partner and didn't share it with the rest of the world, would that be of concern? Or what's this? Publication?
- [07:44]
Eugene Volokh
Yes, that would be liable the public. There is a publication requirement in libel law. But as with actual malice, lawyers have this habit of using words in ways that differ from how ordinary humans use words. Publication for purposes of libel law merely means communication to one person other than the person being defamed. So if you write a letter to a friend saying some third party has done these bad things, that could be libel. Classic examples of that kind of libel were historically letters sent to someone who's about to get married saying that their prospective spouse has committed various kinds of misconduct Another example, which is very common today, or I shouldn't say very common, but it's a fact pattern that we continue to see today is job reference. So somebody says, oh, I wouldn't hire this person because he was fired for stealing from petty cash. Or even he has acted incompetently in some particular specific way. Even if it's said to one person, the prospective future employer, that could very well be libelous or perhaps we may say more broadly defamatory, because similar rules apply to slander, which is oral defamation.
- [09:14]
Kevin Frazier
And thinking about just passing a letter on to someone. What if I qualified and I say, eugene, I really wouldn't recommend hiring Alan because I've heard from other people. I can't verify this, but I've heard from other people that Alan's jokes are just the worst and you're going to tire of them very quickly.
- [09:33]
Eugene Volokh
Well, depends if you're going to. If I'm hiring Alan for. For a job as a comedian, I wouldn't recommend it.
- [09:41]
Kevin Frazier
But let's just. Let's just say for the sake you are.
- [09:45]
Eugene Volokh
The only reason I quibble about that is that not every statement that is negative about a person, you're not even every factual assertion that's negative about a person is defamatory. It has to really threaten their reputation in a fairly serious way. And one classic way in which it could not. The only way by any means, is by suggesting they're incompetent in their profession. By the way, one other factor is it has to be a factual assertion. And statements that I don't like as jokes or even the jokes are very bad jokes, is almost always going to be seen as a matter of opinion because humor is a matter of opinion. Opinion. On the other hand, if I say I wouldn't hire this person because rumor has it that he was fired from a previous job for getting drunk and physically attacking a customer. Factual assertion, something that would indeed materially injure the person's reputation. And in part because it suggests that commit tends to commit crimes and is also not competent in his job. Yes, that would be potentially libelous, even if you qualify it with rumor. Has it that I oversimplify here. Some courts have departed in some measure from this. They may say, well, you know, if there is such a rumor, then then you're not saying something false when you pass it along. But the predominant view is that passing along a rumor, even while saying that it's a rumor is generally actionable. There are actually some exceptions and situations where you should be entitled to pass on Rumors usually kind of one to one communication to people you have a relationship with rather than a statement to the public or to strangers. So again, it's a complicated body of law, but generally speaking, a disclaimer that says, you know, this might be false, but I'm going to pass this along anyway does not prevent defamation liability.
- [11:55]
Kevin Frazier
And just to stick on that idea of a disclaimer for a little bit longer, let's say I'm a particularly cautious lawyer and I am very fearful of being sued for defamation. So anytime I pass, every text I send, every email I send, I say I, Kevin, am unreliable. Sometimes I make things up. So don't trust anything I say in this email. Don't assume that it's factually accurate. Would that allow me to get by defamatory statements?
- [12:25]
Eugene Volokh
I very much doubt it. I mean, I don't know of any case law on point because very few people actually are that candid about their, their lack of reliability. But again, I think it's the same principle as rumor has it, when you're passing along an assertion about someone, even if the listener understands that they can't take it to the bank, it could still be quite damaging to that person's reputation. For example, someone considering whether to hire someone might say, look, you know, maybe there's only a 60% chance that the accusations that were passed along me are, are true, but I don't want to run that risk, especially when I can hire someone about whom these accusations haven't been made. So as a general matter, I mean, I think human beings understand that other humans are often unreliable. Sometimes there may even be a signal such as rumor has it, or I've heard that, or they say that that kind of accentuates the, the, the possibility that the statement may be unreliable, but that is generally not enough to avoid defamation.
- [13:35]
Kevin Frazier
Okay. And so shifting a little bit to where we've seen concerns about libel pop up. Well, since the dawn of the Internet has been our social media platforms or Internet forums where we've seen folks go to that blog or go to that social media site and, and libel someone make some factual assertion that may harm their reputation. How have those platforms managed to evade liability? If you could walk us through, when does section 230 apply and what is the general kind of values animating this idea of Section 230?
- [14:13]
Eugene Volokh
Yeah, so of Section 230, which is Section 230 of Title 47 of the US Code provides for pretty substantial immunities for online platforms when they're passing along material produced by others. So C1, which is the most relevant section, says no provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider. So if somebody posts something on Facebook, Facebook is the one that's distributing it to the world. In a sense, it's Facebook's actions that cause the most damage in many ways, because if the person just posted it on his own, on his own computer, virtually no one would see it. But Facebook would not be liable because this, it was information provided by another information content provider, that is to say, the user. So the theory is sue the user who created the the information and not the platform that is merely redistributing the information.
- [15:25]
Kevin Frazier
And to get behind the original impetus for section 230, can you walk through how some of these concerns about chilling speech really brought about the. The move for Section 230?
- [15:37]
Eugene Volokh
Sure. So like all statutes, Section 230 is animated by multiple concerns. But one of the concerns was indeed that if platforms were held liable for material that's posted by their users, then they would be, they would have too much of an incentive to take it down or maybe never even put it up. So one extreme might be platforms may just go out of business or never go into business because the risk of liability is too high. They can't get insurance because of that risk of liability and such, or perhaps somewhat more likely that go into business. But the moment someone sends a complaint saying this is libelous towards me, they would, they would do this calculation and say, well, if we take it down, then we alienate a user, but we're not going to be legally liable. Among other things, our terms of service say we can take down anything we want, anytime we want. But if we leave it up, then we might have to pay hundreds of thousands of dollars or maybe more to lawyers to defend ourselves. And if it turns out that the statement really was mistaken, how do we know the user is the one who's made the assertion? They're the ones who have the facts. If it turns out it is mistaken, we could be on the hook for millions of dollars for defamation liability. Recently we've seen potentially almost a billion dollars in defamation liability. That was a settlement in one of the cases brought by election machine company saying that it was defamed. There's also a 2/3 of a billion dollar verdict that was recently entered against Greenpeace for allegedly participating in defamation of companies involved in the North Dakota Pipeline Project. So, so as a result, platforms would say, look, you know, the moment somebody files a complaint, we're going to take stuff down. And that would mean that entities that are willing to be litigious could be, could be individuals, could be businesses, could be, could be non profits, could be churches would be able to get to get criticisms removed. So as a result, and again, there are other, there are other concerns involved, but as a result, Congress said, look, we're not going to completely eliminate liable liability for online libel. We're just going to put it, put it on the shoulders of the people who actually posted the material.
- [18:19]
Kevin Frazier
And generally, can you frame how that may comport with again, obviously, with the caveat that there are a lot of values that are baked into the First Amendment or mapped onto the First Amendment. How is Section 230 generally framed as fitting in with some of the broader narratives we talk about when we talk about this?
- [18:38]
Eugene Volokh
Well, it's complicated. So let's go back to New York Times v. Sullivan, the most famous libel case of them all, 60 years old now, but still good precedent. And it concluded that libel law was substantially constrained by the First Amendment. Throughout American history, it's been understood that libel liability has to be judged by standards of freedom of expression. Historically, the Court's conclusion had been that libel law is consistent with free expression principles. But New York Times v. Sullivan said it needed to be cut back. But how far? So the majority, which was six Justices, said that when you're speaking about matters of public concern regarding public officials, you should not be held liable for defamation unless you know the statement is false or know the statement is likely false and are just recklessly published despite that. The knowledge or recklessness standard, again, sometimes confusingly called the actual malice standard. But three Justices would have gone further. They would have said that's not enough to eliminate the chilling effect of libel or the deterrent effect on libel on publishers and speakers, because even with this heightened standard that's required for the plaintiff to prove, still a lot of times newspapers and other speakers will be deterred, will be unduly deterred from publishing even things that are true for fear that a jury would say it's false and that a jury would also find knowing a reckless falsehood. So they would have completely categorically eliminated libel law, at least as to matters of public concern, I think maybe only as to public officials. But the logic of the opinion seems to suggest standing matters of public concern. Those are Justices Black, Douglas and Goldberg would have taken that view. But the majority rejected that view. The majority wasn't willing to go that far. Majority written, by the way, by someone who Generally thought of as an arch liberal, Justice Brennan, who had long been a protector for free speech. So the First Amendment provides for considerable protection for speech, but also aims to retain some considerable scope of libel law as to, again, false statements that damage people's reputations. Section230 in some respects is similar. It too tries to draw a line that aims at protecting speech, but at the same time not completely eliminating the defamation liability, liable liability. But it draws the line somewhat differently that under New York Times v. Sullivan, probably we can't be sure, because Section 230 prevented these cases really from coming up to determine First Amendment liability. But under New York Times v. Sullivan, probably social media platforms would have some liability once they're on notice. Once they know a statement is false, they know of the statement, they have been alerted to what makes it false, they know it's false, or at least likely false. Probably there would have been subject liability there, which would have created sort of a notice and takedown type of regime, not necessarily a great regime, but that's probably what it would have led to. Section230 goes further in protecting platforms even more, and therefore, in a sense goes further in undermining libel protections even more.
- [22:25]
Kevin Frazier
And important thing to point out for our gunners, but for the rest of our students, always read the dissent. Right. Uncovering some very interesting threads here. And I think, Eugene, one thing that stands out to me is how this mapping on of dignity concerns has been a key consideration under the First Amendment for decades, if not longer. We don't need to go all the way to the Founding just to see the importance of those dignity and reputational interests to balancing some of these various considerations. And so taking all of that legal foundation and now moving into the AI.
- [23:06]
Eugene Volokh
Context for just a moment. Yeah. Just please to balk a little bit at framing of this as dignity.
- [23:14]
Kevin Frazier
Yes.
- [23:15]
Eugene Volokh
The defamation is often called a dignitary tort or sometimes called a dignitary tort. One of the dignitary torts. But as a general matter, speech that merely injures someone's dignity is constitutionally protected, at least on a matter of public concern. We see that in cases like Hustler v. Falwell, involving the scurrilous cartoon parody cartoon, trying to, trying and succeeding, perhaps, in injuring Jerry Falwell's dignity. Snyder v. Phelps, which was the funeral picketing with really nasty messages about soldiers, about gays, that's sort of like the line people most remember is they had signs saying God hates fags. Right. And this was a thousand feet away from him, from military Members, figures, funeral. You know, that's something that might be seen as very seriously damaging people's dignity, but that is constitutionally protected. So it's not so much just dignity as protection against false statements. And protection against false statements not only is harmful to the plaintiff. Excuse me, false statements are not only harmful to the plaintiff, they're also potentially harmful to public debate. Right. So this is one of the things that people have been talking about with regard to, for example, false statements about election results and such is they could undermine public debate, undermine the search for truth, because they are false. Now, not all such statements are constitutionally unprotected because there's real danger in restricting even false statements. But when the combination of undermining public debate and damaging a person's reputation, that is something where the courts have recognized, there is a substantial room still left for defamation.
- [25:10]
Kevin Frazier
And moving into the AI space, you provide appendices full of case studies of where we may see libelous statements generated by AI tools. So in the introduction itself of the article, you outline asking a model, prompting a model to detail for you the criminal rap sheet, the crimes that a RR, you use an individual's initials, what this RR has done, and ChatGPT, or whichever model you were using, reports that there have indeed been allegations of criminal activity by rr. So what makes libel analysis complicated in the AI context? If we could just start with what are some of the key issues that don't allow us to just say, oh, okay, well, we knew what libel looked like in 2022 before chat GPT 3.5, and we know what it will look like after it. What's the, the complicating factors in this analysis?
- [26:19]
Eugene Volokh
Well, so each one of the elements, obviously lawyers are going to be fighting over. I think some of them should be pretty easy to establish, but some people might disagree. So, for example, people are aware that AI models sometimes hallucinate. There are disclaimers that the AI provides. Now, at the same time, of course, they, they are seen as sufficiently useful that search engines now automatically include AI generated output in, at the very top of what they generate often. So, so I think that, that those disclaimers are not going to be enough to completely, completely preclude liability. If the disclaimer said, look, this is fictional, this is just, this is just a joke we're putting together, like, I don't know, Magic 8 Ball or something like that, then that might be enough. People say, okay, this is obviously, obviously fiction. But if the disclaimer simply says there might be errors here, that's Generally not enough. Likewise, I don't think there's Section 230 immunity for the platforms.
- [27:31]
Kevin Frazier
Before we move on to Section 230, just to hang on to this idea of disclaimers, because I think a really good point you make is it would be one thing if the models were saying, or excuse me, if the AI labs were saying, hey, you know, we're generating a new eight ball that you can shake and it's going to come out with outputs and ha, that was funny, right? It says they did commit a crime or they did, you know, break that person's foot, whatever. But you point out that these labs are quite invested in making reports and press releases about, look how well it did on the bar, look at how it's replacing doctors, look at how you can rely on this to replace that intern. So it's not as though they aren't trying to make these as accurate as possible. So I think that does a lot of work for your argument.
- [28:23]
Eugene Volokh
Exactly. I think you've hit the nail on the head with that. I think it has to do with the way that the AI companies themselves are promoting, among other things, in the course of justifying the tens of billions of dollars that have been invested in them, they're promoting this as something that is not completely reliable. But you know, nothing in the world is completely reliable. They're, they're promoting it as it's reliable enough that you should use it. That's. So then it's unsurprising that people would view it as reliable enough that they might refuse to do business with someone because of something that that is out propellant.
- [29:00]
Lawfare Host
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- [33:22]
Kevin Frazier
You know, expertly too, that it's especially in when you're considering, oh well, maybe I'll go to this one specific doctor or maybe I'll go to this one specific lawyer. If you're using generative AI to get an assessment of, you know, I want to know what are Professor Frazier's class rankings and what crimes? What's his rap sheet? What's his background like? All it may take is that one generated response that says Professor Frazier did X, Y and Z for one student or one prospective student to say, huh, maybe I'm not going to sign up for that class or maybe I won't go to that school. And so this publication question too is a really interesting one in terms of thinking about who the output is actually being shared with and what the actual response may be to that output.
- [34:14]
Eugene Volokh
Right. So I actually don't think that the publication element of Libelo is going to be much of an issue here in, in situations where at least somebody else has run the query and has, has seen the output. And sometimes of course, the AI companies may have logs of who has, who has run what queries. So if I run a query and it says something about me and then I sue based on that saying this is all false. The, the defense is, well, wait a minute, only was output to you. You can't damage your own reputation with yourself, right?
- [34:53]
Kevin Frazier
You've got a pretty good reputation. So, you know.
- [34:56]
Eugene Volokh
Pardon?
- [34:57]
Kevin Frazier
I said your reputation's pretty sterling at this point. So.
- [35:00]
Eugene Volokh
Well, but it doesn't matter because whatever people, other people might believe about me, that's false. Presumably I won't believe things about me that are false except in highly unusual circumstances which the law does not focus on. So in that situation, publication requirement would be absent. But so long as other people are running this query and seeing this output, then I think the publication requirement is present again. Remember, it doesn't have to be broadcast to the world at once in the same form if it's shared with a bunch of people one at a time here and there. Which by the way, is the way websites are visited Too right. They're just shared with each individual user as the user goes there. That's enough for publication. Even if just shared once with somebody, passed along once to somebody other than the plaintiff, that is, generally speaking enough for publication. Section230 Also, I think will not be that much of a barrier to liability because remember, it says no provider or user of a interactive computer service shall be treated as publisher or speaker of information provided by another information content provider. But the whole point of Generative AI is that it's generative. It's that it's generated by the AI company's products. So the lawsuit would be against the company for passing on information that's generated by itself. So the premise of section 230 is don't go after Facebook, go after whoever posted the thing on Facebook. Well, here if it's chat GPT, it's OpenAI that is posting the material to the user. So Again, I think Section 230 would not be much of a barrier. We can talk about some of the other things, but I think is really the. The issue here has to do with mental state. So remember, modern libel law, generally speaking, concerns itself heavily with the speaker's mental state. If the plaintiff, the person who allegedly was, whose reputation was allegedly damaged, was a public official or public figure, the plaintiff has to prove that the defendant knew the statement was false or was like, knew the statement was likely false, was reckless about that. If the plaintiff is a private figure and can show actual loss, not just hypothetical likely loss, but actual loss, then the plaintiff merely needs to show the defendant was negligent, was careless in its investigation. And actually, when it comes to speech on purely private matters, maybe there could even be strict liability. But let's bracket that. That's pretty rare for a variety of reasons. Well, what does it mean to ask about the mental state of a computer program that has no mens. No. No mind? Right. Mens. Rea. Guilty mind. Well, it has no mind. It can have no guilt. So what does that mean? And I think the answer has to be that we look at the mental state of the organization that is, that is responsible for the platform that has. That has created the code and that is operating the code. Now, by the way, that's complicated because sometimes it could be quite different. Somebody, let's say, puts out a public domain large language model and other people are operating. Interesting questions. Let's bracket that for now, though. I talk about them in the article. But if let's say it's ChatGPT was created by OpenAI, it's being operated by OpenAI. If the question is knowledge or recklessness, question is what does OpenAI know? Now at the beginning, presumably it knows nothing about particular individuals who are being discussed, as it were, by its software. It doesn't even know that. I mean, maybe again, yes, somebody's going to be asking about Donald Trump or Bill Gates, right? But he doesn't know what's being output about. But let's say somebody says look, your software is outputting material that's false about me. And I realize you didn't know that, but now you know I told you. In fact, not only did I assert this, I actually send along a printout, you can check it against your logs and I sent along supporting data that shows that this is just not true. So let me give you an example. There is a case pending, although probably it's going to end up being disposed of in arbitration as an arbitration agreement where somebody named Jeffrey Battle says, oh, Microsoft is outputting information about me that, that, that reports that I was, that I was convicted of, of serious felony and sentenced 18 years in federal prison. And that's not me, it's another person with the same name. But it's linking the two of us together because the output begins by describing my actual current job, I'm an aerospace expert. And then says however comma, Battle do these other things. And I can show you there's a Wikipedia entry that obviously the answer was drawn from the first part of the answer which describes me. There's another Wikipedia entry that describes somebody else with the same name and the libel is in reporting that the two are the same person. So open and shut not one of those he said, she said situations, right? So at that point the company in this case it was suing Microsoft would know that this is so and would be able to do something about it. Now, apparently untraining or retraining large language models to sort of tell them stop saying this is, I'm told, technically very difficult. But large language models aren't the only kind of software, right? I think any of us could easily design software that says, okay, after output is generated, look up any things that you can identify as names. And generally speaking, there are algorithms that pretty reliably identify whether something's a person's name. Look them up in a list of known falsehoods that have been set by the softer about them and if indeed the name appears within the same sentence as felony or was the accusation was, excuse me, the, the other Jeffrey Battle was convicted of levying war on the United States. So if it appears within the same sentences as that or same paragraph, then just don't produce this output. I mean that's, that's not difficult code to write. It's over and under inclusive. It won't catch everything and it may block things that, that are not false. But maybe that's what's called for. If you're going to let out into the wild the software that can generate potentially very harmful assertions, you'd need to have these kinds of controls there. And in fact I am told, I've seen news accounts that indeed sometimes if you, if you put a person's name into, into particular, particular AI program, it just refuses to give you an answer. And that's in a sense a chilling effect. Right, but the theory is better to be somewhat chilled there than to output something that you know is false and that, that it turns out it's been reported to you that this information, that there has been false information written, written about the person. So that may be a somewhat like 1.0 version of this kind of control mechanism. Presumably you'd want to have something that is more kind of, more, more carefully tailored. What about negligence? Well there it turns out that we have a decent amount of experience with negligence liability for machines and for software. Software that's usually filtered through the law of product liability and design defects. I oversimplify here, but basically if I am injured by a self driving car, let's say Waymo, I'm walking down the street and a Waymo hits me. I wouldn't sue the car, right, obviously. But I could sue Google which runs Waymo on the theory that there was negligent design, that they, that the software didn't recognize me as a pedestrian and there was a better design that would have, would have prevented that. And then of course there'd be a battle of the experts as to well, would it be effective design or not? Not a great thing for lay jurors to decide. But that is the way our tort liability system works. So I think those are going to be the complications. The question of ascribing mental state in these kinds of situations where the output immediately is created by a thing that has no mind but is ultimately the responsibility of an entity that's populated, that's staffed by people who do have minds.
- [44:14]
Kevin Frazier
Well, there's, there's a lot to unpack there. I want to start quickly with just this 230 argument. You have said that you don't think 230 would apply. You outline a great case in your paper. What's the strongest argument you've heard for why Section 230 should apply to AI models and how would you refute that?
- [44:34]
Eugene Volokh
Right. So I have to say I haven't heard any really persuasive argument as to why Section 230 by its terms does apply. I mean some people have said well, really large language models are all based on training data. So really you're holding them for information provided by the source of the training. That. But in most of these cases the training data does not contain those false assertions. If it's true. If the training data says Eugene Volok was convicted of stealing from petty cash and that's why he was fired from ucla just to make clear that it's not.
- [45:15]
Kevin Frazier
So the Bruins didn't kick you out for that reason. That's good to know.
- [45:19]
Eugene Volokh
Right, Right. Nor was that amicable. Amicable retirement from teaching. But let's say there is the training that it's false. But this. But the software sucks it up and then re outputs it. That's the garbage in, garbage out scenario. Then it may be maybe immunity section 230. But the problem with large language models is it's sometimes gold in garbage out. Right. All the training data may be perfectly accurate, but the output is still false because it's weirdly recombines words. Not even recombined. I mean in a sense all output is recombining words that already exist. But it's responsible for how it puts the words together. That is the, that is its speech. So it would be held liable. So, so that's. So I don't think there's a statutory basis for statutory construction basis for saying section 30. There's a policy argument that basically is look, we should have something like section 230. Maybe we should create a new section 230 because we don't want to have an undue chilling effect. We don't want to deter the creation of the software and we don't want to encourage it to over restrict the way that apparently it has been doing in some measure again by saying look, we just won't answer any questions about a particular person. So. So we should have a new section 230 that does that. The problem is that would essentially be saying there's nobody who'd be responsible for that and that if people's reputations are damaged, well too bad for them. And you know, that's a possible policy decision. It's just I'm not sure that it is a wise policy decision. Especially since some the of of these Companies are extremely wealthy. They have the tools to try to make their software better. And to the extent that let's say they, it's even technically impossible to guarantee perfect safety, well then maybe the answer is they wouldn't be held to be negligent if sort of a product design argument. And even if they are held liable for something, well, you know, that's cost of doing business and they should factor it into their, into their financial analysis and that may encourage them to, to produce more reliable, more reliable output. It's again, it's in a sense like self driving cars. Self driving cars I think are a wonderful thing. I ride in Waymos whenever I can. They're only available in some places, but I'm happy, happy to use them. But I don't think anybody says, well in order to encourage the development of self driving cars, we should make them categorically immune from any harm that they cause.
- [48:06]
Kevin Frazier
So that's, in that instance, I would say every San Francisco resident, hide your kids. But that's another conversation.
- [48:13]
Eugene Volokh
Well, the thing about self driving cars is they're probably better for society because they're safer than humans.
- [48:20]
Kevin Frazier
Oh.
- [48:20]
Eugene Volokh
So don't get in some measure avoid undue discouragement of self driving cars, but at the same time I think the answer is to provide a sensible level of liability rather than giving them complete.
- [48:33]
Kevin Frazier
My short remark on that is anyone who's opposed to autonomous vehicles, come drive in Miami and you will become the most rapid supporter of AVs known to man. But that's another podcast we'll save for another day. Eugene, another point you make is that a lot of these libelous outputs from models tend to be quotes. Tend to just be Professor Volek said quote in quotes X, Y and Z. And that's obviously just a slam dunk, easy liable case. So you create some innovative and very straightforward solutions to this quotation issue. Can you just walk through those, those mechanisms?
- [49:15]
Eugene Volokh
Yeah, sure. So I should say in 2023 when I wrote the, the article, often these programs would output things in quotes, which is extra dangerous. Right. Because quotes are sort of signals to us. I oversimplify here. There are scare quotes, there are quotes used in obvious fiction, but generally speaking, in many contexts there are signals that essentially say we're actually reporting on something somebody else wrote and that makes them extra hazardous. If I see a paraphrase phrase, I might say I need to check the source. If I see a quote, probably going to be a little bit more likely to trust it. But apparently what was happening is the software was just treating a quotation mark as any other kind of token and if it predicts that the following token is going to be a quote even and then it just includes that and then includes whatever the next token it thinks is. Even if it never appeared in the training data without any attempt to verify the quotes, let's say doing a Google search, seeing if it's quotes appear somewhere and such. Now in more, more recent years as in more recent months as I've been using the, the software, I've seen it, I've seen a lot fewer courts, not none. I actually was just doing an experiment with a student of mine where I asked the, the, the. I think it was, yeah, it was definitely ChatGPT4. I asked of a legal question and it gave me actually the correct answer, citing the correct case but giving a quote that did not appear in the case. So it was generating hallucinated quotes. So one possibility might be one possible. Actually let me step back. When somebody says there's a design defect in, in a product, usually again I oversimplify but usually what that means is that the product was negligently designed in that there was some relatively cheap precaution that could have been taken but wasn't taken. So a self driving car, they could have just by adding this particular piece of code they could have recognized that this blob going across the field division was a pedestrian, let's say. So likewise what you're looking for, what you would be looking for if this issue came up in a real case involving large, what I call large libel models, then what you're looking for is is there a way that they could have diminished the risk of this harm. And one possibility is have a code that says do not output quotation marks unless the things between quotation marks appear somewhere either in the training data or if you don't have access to the training data, appear in some corpus, maybe do a quick Google search and see if you can find them. And if they don't, then just don't include the quotation marks because then you can't vouch for it for the accuracy. Get their complications. What if it's quotation marks in fiction that the AI was asked to write? But you know, one of the things that I think the AI companies will have to recognize if they make all these claims. Oh well, we just too complicated for us to implement these fixes is they have to say yes, we can create software that performs at the 90th percentile on the SAT and on the bar examination and this and that, but checking to See if the quote actually exists somewhere. Oh, too difficult, right? I just don't think that on the facts the, the AI software developers will get away with that kind of argument.
- [53:04]
Kevin Frazier
You mentioned that you could foresee for the largest labs, this being a sort of cost of business of updating their software or updating their systems to make sure we're checking for these sorts of liable statements. How do you respond to concerns that mapping this sort of requirement onto AI systems may quash AI innovation, may make it unduly burdensome for smart.
- [53:29]
Eugene Volokh
It is a very serious concern and it is concerned with any product. Right. Any service as well. Medical malpractice liability may undermine possible innovation in medical practice because usually doing what everybody else is doing is likely to be seen as reasonable. Whereas trying to do something better if, if things go badly, even if it's not really your fault, that may be seen as unreasonable. Not your fault in the sense that you, you, you had really good reason for doing it, but the result was in this particular instance, bad very much. It's a very reasonable fear that your actions would be seen as unreasonable. So likewise with regard to getting self driving cars, I think Tesla and Google can afford the risk of liability. But yeah, if somebody wants to create self driving car kind of in his garage and sell it to people a lot more cheaply let's say than a Tesla is, is sold. Well, I guess I'm not sure how full self driving Teslas are but certainly that's the goal and Google's Waymo is fully self driving. Then then in any, I'm sorry, the risk of liability may deter this, this, this startup and not even just in the garage. If there's somebody is looking for, for investors, they may say well wait a minute, you know, we don't want to invest all this money and all of this will go, go to the lawyers and go to verdicts against you. Very serious concerns. But on the other hand, it's also a serious concern if innovators are not held responsible for the harm that their innovative products cause because then they may just not act as safely as possible. Maybe in fact I shouldn't be creating self driving cars in my garage. Maybe I shouldn't be letting lose a language model that I know people will use to make decisions if the model just makes stuff up about people. By the way, this issue has led to some statutory action in some contexts. So medical malpractice recoveries are capped in some states and there's some procedural rules that are aimed at not unduly deterring reasonable Behavior as I understand it, nuclear, nuclear power plants or nuclear power plant operators have their liability capped at some many hundreds of millions of dollars, but still have it capped in order to avoid deterrence of nuclear power. Now that there are a lot of people, are a lot of environmentalists are not speaking out in favor of nuclear power because it's ultimately cleaner than the alternatives. We might be seeing that, that becoming an important protection again for new power plants, but very rarely is the rule, well, we so want to promote innovation, we'll have no liability whatsoever. Right. Usually if the legislature steps in, it tries to balance these concerns. Just like with Section 230, it didn't completely preclude defamation liability. It just said it has to be on, placed on the original speaker. Well, if you are going to preclude liable liability even for the original speaker for the entity that's responsible for generating the output, well, there need to be a, a legislative judgment, I think along those lines and probably either the legislature will say no. If anything, a lot of people think section 230 itself already goes too far. I'm not sure that's right, but, but I think that's the sentiment among many. But at the very least they probably say, look, there's got to be some sort of compensation, some sort of mechanism for protecting people, innocent third parties whose reputation may be damaged and who may be economically ruined potentially as a result.
- [57:32]
Kevin Frazier
And shifting our perspective to what's on the horizon. One subtle part of your paper touches on considerations of the use of open source models, so models being used by downstream developers. And I think one question I'm particularly keen to know how you're initially thinking about is the idea of AI agents so we can have agentic systems where it's an AI agent talking to another AI agent talking to another AI agent who then shares an output and posts that, let's say, on your LinkedIn and you never even thought about what it was going to post or when it was going to post it. So in these instances of multiple entities or individuals relying on multiple AI systems, how complicated is all this going to get? Do we need to start thinking of wholesale reforms to our conception of libel or do you think that this pre existing structure can be amended or adapted enough to fit this crazy technical world we're living in?
- [58:37]
Eugene Volokh
Right. Well, it's hard to know for sure, among other things that we're. It's still early days at this point. I know of two lawsuits that are being litigated in US courts. One, the battle case, which again has been shunted off to arbitration. It's in federal district court of Maryland, another one in state trial court in Georgia where the, where OpenAI's motion to dismiss was actually denied by the court, so the judge allowed the case to go forward. Although it's still not a trial yet. There's also a complaint that's been filed recently in Norway with the Norwegian data protection authorities about libelous output, accusing actually a Norwegian man of killing his own sons. The good news is everybody's alive and well. But the bad news is he's saying, look, you know, it's making up very, very serious allegations about me. But still it's only three such instances that I know of, plus a few others where the liability has been. Lawsuits have been threatened, but the only three filings that I know of. So probably there won't be a lot of movement for massive reform until we see some decisions there. At least we see how courts are handling this right now. I will say as a general matter, our legal system is quite well acquainted with harms that are, that stem from a combination of actions by many parties. That's sort of a staple of first year tort law for those who have taken it. Just remember a lot of times the lawsuit is, let's say some train causes somebody's, some vendor's cart to, to. Well then actually, I'm not sure to train. Let's just say some, some bus causes a vendor's cart to tip over and then there's. That doesn't damage the, the goods, but as a result thieves come and steal the goods. To what extent is the bus operator responsible for, for the theft of the goods? Well, the answer is maybe even though it's a third party, it may be the negligence of one enabled and the intentional misconduct of another. And then you can multiply it further, especially when you get a product liability. Historically, you know, there's been the, the seller, there's been the manufacturer, but the manufacturer may have bought parts from many other people, right? Could be a contractor and a bunch of subcontractors. So the legal system is familiar with that. It may be that it'll map the existing rules in a way that doesn't make sense onto this new technology. And if that's so, then I think quite possibly Congress will step in or state legislatures in some situations will step in. But for now, at least, I think the answer is going to be that courts will be applying these familiar rules developed over centuries having to do with liability of multiple causal factors, as it were. Parties that caused things in variety of different ways. Into a variety of different degrees that will try to map them onto AI.
- [62:00]
Kevin Frazier
And before we let you go, we have some pre law students I'm sure who are watching this. We have folks who are decades out of law school who have maybe moved on to thinking about the black letter law, but are really involved in, in theory and policy. What are some things that are top of mind for you that if you were to reach out to folks who are curious about diving deeper into these issues, what questions do you recommend they look into or some, some cases or future trends that you think are particularly worthy of their attention?
- [62:31]
Eugene Volokh
Yeah, you know, really hard to know. Really hard to know. I did not anticipate in 2022, I did not anticipate what Chad GPT would be doing. It's very hard to predict what, what's coming down the pike. Among other things, there are, there may very well end up being lawsuits over physical injuries a result of AI. There's of course a lawsuit pending right now involving a suicide of a teenager who was involved in chatting with AI and for whatever reason as a result of the output, the claim is committed suicide. That those kinds of cases are percolating up, especially when children are involved. Usually pretty hard to hold an entity liable for someone's suicide. And in those kinds of cases I'm actually pretty skeptical of liability, but courts are going to have to deal with it. And then on top of that, of course, or just the thing that one might be thinking about is what if there's other kinds of physical harm? For example, people follow the medical advice of an AI and that advice is provably false. Like there's a log that says you should do this and that and that that clearly is not the right thing to do. And it was indeed what the person did. So, so the causation may be pretty straightforward. To what extent would there be that kind of responsibility? So and you're quite right that that the agent environment where lots of things are happening, like we let something loose and we think we know what's going to happen and it turns out the result is vastly broader, at the very least vastly different than what we'd expected. The legal rules may end up being familiar. Is it careless, was it harm foreseeable and such. But how they'll actually play out as a practical matter maybe maybe quite surprising. And again because it's surprising difficult to predict.
- [64:33]
Kevin Frazier
Well folks, we're going to have to let class out and allow you all to get to your homework. But for now thank you so much Eugene for joining the AI Summer School.
- [64:43]
Eugene Volokh
Thank you so much for having me.
- [64:48]
Kevin Frazier
Scaling Laws is a joint production of lawfare and the University of Texas School of Law. You can get an ad free version of this and other lawfare podcasts by becoming a Lawfare Material supporter at our website, lawfairmedia.org support. You'll also get access to special events and other content available only to our supporters. Please rate and review us wherever you get your podcasts. Check out our written work@lawfairmedia.org you can also follow us on X&BLUESKY and email us@scalinglawsawfairmedia.org this podcast was edited by Jay Venables from Goat Rodeo. Our theme song is from Alibi Music. As always, thank you for listening.
- [65:44]
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Kevin Frazier
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