Are We Too Obsessed With AI Predictions? — With Carissa Véliz

24:25And so it was it was too confusing and they couldn't do it. And again, even though there is no checkbox and no easy way to tell which predictions are acceptable and which are unacceptable. One thing to take into consideration is, is this a prediction about a thing, floods of things or about something more social?

24:46Big Technology Podcast Host:Yeah. Well, let's go with let's go back to the pandemic example. So, that's the first I'm learning about this Google example, but there are other versions of, you know, prediction, AI-based prediction that are helpful when it comes to pandemic. Wastewater analytics, for instance, is really interesting where like there are companies, we've had them on the show actually, that can, let's just take the COVID example. they see how much COVID or how much virus there is in the wastewater and then they look at the rate at which it's advancing and then they can actually predict a spike that could free people because if there's like no if there's no prediction involved and like when the spike is going to be like the answer might be locked down everybody the other side of it is if you can predict that there's going to be a spike you can be selective in when you want to shut things down versus open them up.

25:37Yeah. And one important thing is the closer you are to the present, the more likely your prediction is reasonable. So if you hear somebody predicting what's going to happen in a thousand years, take it with a big, big pinch of salt, or in fact, just kind of laugh it off.

25:51Big Technology Podcast Host:Now, the people that come on this show, they won't predict like a year into the future because this AI world is changing so fast. But yeah, a thousand is... But like long-termists in effective altruism are thinking about the world a thousand years from We got it. Yeah. Or, you know, some people are thinking about the world in 50 years or 25 years. So the more you ground yourself in the present, like if you see the analytics of the wastewater right now, that is very useful information. And depending on how much you know about the virus and how much experience we've had, you might be able to make some useful predictions.

26:24Now, that doesn't mean that we will be able to predict the next pandemic. It might be a virus that we've never seen before and we don't know how it behaves. And one very important kind of warning is beware of people who will promise a prediction in exchange for huge surveillance. Because the price to pay for mass surveillance is a police state. It leads to authoritarianism. And so often we're willing to surrender our privacy on promises that are never kept, that are very problematic even if they could keep it. And we're sort of selling our democracy.

27:01Big Technology Podcast Host:Okay, but we need an example here. So, like, where is the surveillance happening? All right. That leads, where are these tradeoffs? So, I used to live in New York City, and I hadn't been in the city for a while, and I've noticed how there are many more cameras than when I used to live here. Right. Many people claim that, well, we need the surveillance for safety. The more surveillance we have, the more safe we are. Right. But that is empirically inaccurate. So the safest countries in the world are not the most surveilled ones. So Spain is one example. It has some of the lower statistics for any kind of crime, including homicide and so on, violent crimes.

27:45And it's not better surveilled than the U.S. or the U.K. And in fact, the U.K. is the country in Europe that is most surveilled and it has more crime. And so that's one example. But it's important because when you have a protest and in particular a peaceful protest, it's very important to have anonymity. That is one of the bedrocks of democracy. And when you have cameras all over the place and now with facial recognition being so easy to use, you are eroding one of the most important tools in the toolbox for democracy.

28:21Big Technology Podcast Host:I have so many questions about this. I mean, first of all, I'll just say, like, have you been to China? I've read a lot about China. Okay. I was in Beijing for a day. Right. But that was enough to see the level of surveillance there. Yeah. A lot of cameras. Yeah. Now, there is a feeling that society is safe, but it's not a society I'd want to live in. Exactly. But there is some sort of spectrum there where, like, you probably do want some cameras up So you can, like for instance, a security camera in some areas, that's good, right? Without any video footage, you probably solve less crimes. So isn't it a matter of like finding where on the spectrum you would live, you want to live?

29:05Yes, but I think we're getting it very wrong. I think that the practical question we're asking by having this amount of surveillance is how much surveillance can liberal democracy take? And I'm afraid that we might find out. And I don't want to find out. Because I don't want to live in China either. And the illusion of a world without crime ignores the fact that that would be a world very full of a very different kind of crime, authoritarianism. Exactly.

29:31Big Technology Podcast Host:Yeah, that is a problem. So talk a little bit then about how generative AI sort of comes into this. Because, I mean, I think we kind of hinted at it at the beginning and then sort of went to this earlier version of machine learning that's everywhere. But there is this – now there's a trust towards chatbots. And, yeah, you can really steer your life towards different outcomes based off of what ChatGPT tells you. And it's probably worth at least thinking about that before diving at first, like I often do. Absolutely. And maybe just to end the previous topic, surveillance is important because the whole machinery of surveillance is there to feed the machinery of prediction.

30:19So these two machineries are intimately related, and that's why it matters.

30:23Big Technology Podcast Host:But we're not living in, like, minority report, though. We seem to be walking in that direction, and I would like for us to walk in a different direction. I mean, but we're—let's just talk it through. We're not, like, arresting people on crimes they may commit, are we? No, but we're using predictive algorithms in the justice system for sentencing, for many aspects in the justice system. And for the reasons that we explored with insurance or with loans or with jobs, that's very problematic. We talk a little bit about how those predictive algorithms are used in the justice system. And then we're going to get to this gender of AI question.

31:00Big Technology Podcast Host:But you keep taking us in interesting routes. Well, it depends on the place. They vary a lot. Right. But some algorithms are used to assess the risk of a person committing a crime and on the basis of that, deciding whether they might get bail, for example. Peril. Parole. All these things. All these things. Another case that worries me that I think people are less aware of is whether, for example, an insurance company decides to cover a lawsuit because they only cover a lawsuit if the case has a 51 percent chance of succeeding. And that makes sense in some ways. You can see the rationale behind that.

31:45But at the same time, when we make the justice system about probabilities, we're losing its principled approach. And so you make it very easy for the bad guys to get away with it because you don't have to make it impossible for people to challenge you or even very hard. You just have to make it slightly unlikely for them to win. And then you get scot-free. So there are all kinds of distortions of justice when we introduce probabilistic thinking into an area that I think should be more based on principles.

32:13Big Technology Podcast Host:Okay, I got one more for you. I just want to hear you talk it through. Why there's a right to privacy if you protest. I'll tell you what my fear is. All right. And it's good to just talk it through. If you end up having – now I'll say something negative about algorithms. We have a world where algorithms drive people to extreme positions. The more extreme you are, the more likely you are to get play in the algorithm. And part of that is anonymity, right? You can say these things as trial balloons with anonymity and sort of see how people respond to them and then double down. And I think one of the fears with anonymous protests, and I'm just talking it through, I'm not taking a position here, but it takes some of those online dynamics and brings them into the physical world.

33:14Big Technology Podcast Host:Where like, you can, if you're unidentified, the temptation to move to the extreme or the ability to move to the extreme is further and further. I believe in free speech, but I also think incentives matter. So talk through what you think about this one. Absolutely. I have a paper called Online Masquerade, which I'm going to send to you. And the gist of it is that even though it's very intuitive to think that way, when you look at the empirical data, it shows that people who are identified online tend to be more aggressive and then they tend to be more followed and more successful in that aggression.

33:52And, you know, we see this in the public's domain. We know important politicians who put their name on things and who say very outrageous things, and it works. And so anonymity is not necessarily leading to more aggression or more toxicity. The second thing is that if you're in the public square and you're protesting, and let's say you're protesting peacefully, and there is one person who is aggressive or who is showing something, who does something illegal, then, of course, the police can always arrest them. But we don't need to have mass surveillance in order to have that, to have accountability.

34:30We didn't have mass surveillance a few decades ago.

34:34Big Technology Podcast Host:Right, but I'm not saying the mass surveillance. I'm just saying the anonymity part. Like if everybody protests in a mask, you know, doesn't that – you think that leads to better outcomes than if they don't? Well, we shouldn't need a mask because we shouldn't have this kind of surveillance that identifies us. It's a product of the mask. Yeah, exactly. I hear that. But even if somebody wore masks and they break a glass or whatever, then have the police arrest them and take off the mask. Right. But you can't, I mean, I'll let that sit. I don't want to spend the whole day debating this. But it's interesting to hear you talk about it.

35:10Big Technology Podcast Host:All right. Now talk about the gender of AI side finally. Yes. So if we have these systems of prediction in our world, I mean, again, like people who are building Gen.AI tools, they care very much about prediction, predicting the next word, predicting outcomes. And when they can predict outcomes, then their agents can take the next step. Where is that leading? Yes. So some authors make this distinction between predictive AI and generative AI. And I am not sure it makes sense because both kinds of AI are essentially predictive. We might use them differently and they might look differently. But essentially, they're both machine learning.

35:44And what machine learning broadly does is it has some data and it projects data that it doesn't have based on data that it does have roughly. Whether it's predicting the next word or predicting whether somebody is going to be a good employee or not. And in the case of generative AI, it's fascinating. I don't know where to start. It's fascinating how it got trained. I mean, that's one thing, you know, with copyrighted material, with personal data. And so that's one kind of thing. We can just park it, but just notice. And in the way it works, it's a very sycophantic system, as we know. It likes to please people because that's the way it gets you to be engaged.

36:24And so it will tell you things like, oh, that's a brilliant idea. And it will continually validate you. And they were built, they were designed to do that. They were designed to make people feel satisfied. instead of being designed for another thing, for example, for being truth tracking, which would be much more useful if, say, you're a researcher. And I think sometimes we lose sight of that. And one way to put it is in the philosophy world, there was this philosopher called Harry Frankfurt who wrote a book called Bullshit on Bullshit. And Frankfurt says that bullshit is very dangerous for democracy because the truth teller and the liar are playing the same game on opposite side of the court.

37:11But the liar has to know what the truth is in order to lie and care about the truth. The bullshitter doesn't care about the rules of the game. They're not playing the game at all. And that's very toxic for democracy because it's very hard to have a debate or a conversation with someone who doesn't care about the truth, who will say anything to just have the kind of reaction they want with no regard for the truth. And that's essentially what a large language model is. It has no regard for the truth. It wants to please you. If what pleases you happens to be true, great. But if it's not true, then it doesn't care one way or another.

37:47Big Technology Podcast Host:But is that true? Because, I mean, the labs have done a lot of work to ground these models in truth. And in fact, like, if it was a bullshitter the way that you would explain, there would be very little economic value. But we can see now that there's real economic value. we don't know whether there's real economic value. The jury is still out on that. But yes, the labs have done more. You don't think, I mean, I guess like, it seems to me like we're past that point where there's a real questionnaire. Now, maybe it's not going to be broad economic value in a way that makes the boom appear justified.

38:22Big Technology Podcast Host:But if you look at places like coding, like there are areas where we can see today that there is definite real economic value there. I don't know. I'm not saying there isn't. I don't know. Because sometimes these systems create mistakes that then are very expensive to fix. And it's not easy to make the calculation of whether we are getting economic value. There was a paper recently at the Harvard Business Review that suggested that even when people think they're being more productive with AI, when you have researchers look at it, they're being less productive because they're spending a lot of time fixing what the AI gets wrong and not noticing that.

39:00So I don't know. Maybe we do, but I'm not as – it's not crystal clear to me.

39:06Big Technology Podcast Host:Okay. But even if we do. It's nice to have somebody with a different perspective here. We shouldn't have the same people all believing the same thing. No, of course. And I grant that I don't know. I'm not just saying something. I just don't know. But even if they do, where were we? I mean, this is really the key question about Gen.AI, where your argument is that it's a bullshitter, And I will just throw out there, and this is something I really do believe, these companies are spending lots of hours and dollars trying to ground these models in reality. Because if you do that, they become much more useful, and they've become much better at it over time.

39:47Absolutely. But the interesting thing is the way they become much better has been by getting away from this probabilistic and statistical thinking. So, for example, when you start chatting to a chatbot and then they realize that what you're looking for is for something, say, in a manual, in a PDF, then they refer to the PDF, and that's how they ground themselves in reality. Or when they realize you want a calculation, then they plug into a calculator because these systems cannot calculate, as we know. And so that's interesting that the way to make it better is to move away from this probabilistic thinking.

40:20So part of my criticism is not about AI or any kind of AI, but about prediction, about how we're using prediction and how naive we've been about using prediction. And I think if these systems had been designed differently from the start, they would have needed less patches. And how do we think about this going forward so that we design systems from the start to be truth tracking rather than fundamentally about engaging people for profit?

40:45Big Technology Podcast Host:But how impressive is it that they know, okay, actually my knowledge actually stops here. I should use the calculator or I should actually go look in the PDF. And I would say the argument that the model makers would make is you can't have the tool calling before you have the base model. And it took a couple of years for these base models to get smart enough to know when to call those tools. That sounds great, and I'm on board. Okay. But in practice, they're still not quite there. So, for example, I'll give you a very recent example. It's weeks old. If you ask one of these chatbots, I have a box and I'm going to put two bunnies in it and then five months later I take five bunnies out.

41:31How many bunnies are there? And it will say minus three bunnies. So they still don't have enough understanding to always figure out what they need, right? In this case, they might have gone to a calculator and that wasn't appropriate, right? Because they don't understand that bunnies can reproduce. truth. So yes, with nuance.

41:50Big Technology Podcast Host:Yeah. I mean, there are people, examples of people asking the most advanced models, like how many peas are there in strawberry? And it's used to being asked how many R's are there in strawberry? And it gets it wrong. Exactly. Let's just end this segment. We do need to go to a break, but let's just end this segment sort of with your broad thesis here, which is that like, and you tell me if this is the right way to encapsulate it. We live in a world where there's a lot of prediction, more prediction around us all the time. Prediction in the AI models, prediction that's influencing the jobs we get, whether we get a loan, all these things.

42:32Big Technology Podcast Host:And rather than just take this notion of prediction for granted, we should probably pay attention to the nature of those predictions themselves. Is that sort of what you're saying? Yeah, exactly. Because predictions can be weapons of power. They can be power plays in disguise, and we need to be less naive and smarter about them. Okay. Well, that is all being put on steroids in these prediction markets because oftentimes you'll see a prediction in a prediction market. And the question is, is that somebody manifesting an outcome? Is it someone with direct knowledge of an outcome, or is it actually just a market for what might happen?

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46:07Big Technology Podcast Host:She's an Oxford philosopher and the author of Prophecy, Prediction Power, and the Fight for the Future from Ancient Oracles to AI. Great title. So what do you think about prediction markets, Carissa? They scare me. Okay. That is, after our first half conversation, I'm not surprised. What particularly about them scare you? So the argument for having them is that they can be a source of knowledge, right? When people bet with their own money, and if they get it wrong, they lose, they'll try to get it right. And when you have a lot of people placing bets, in theory, we can harness the wisdom of the crowds, all of which sounds great.

46:41But it assumes that prediction is a kind of quest for knowledge. And it doesn't consider that sometimes prediction is a quest for power. So, for example, if you want to influence public perception and you have enough money, you can bet heavily on something or someone to make it look more popular. And we already have examples of politicians betting on themselves.

47:03Big Technology Podcast Host:Great use of campaign funds. Yeah, exactly. Make it look inevitable. That's what every campaign tries to do. Exactly. And when you start having these prediction markets have deals with newspapers in which newspapers are reporting on the prediction as if it was a fact, then it gets to be a really smart way to invest your campaign funds. Another example which is concerning are ones in which there are many ways to make a prediction come true. And one of those ways is to make it come true after the fact. So I don't know if you read that there was a case in which an Israeli journalist had reported about a strike in the conflict.

47:44And some people bullied him to try to change his story because they stood to win$900 ,000 from a bet.

47:53Big Technology Podcast Host:Yeah. It's like fantasy sports. Yeah. Another case that is concerning is six anonymous accounts earned$1.2 million on a prediction market betting for the attack on Iran. And some of those wallets were funded hours before, which suggests that they might have had insider information. And if they had insider information, did that conflict of interest lead to a different kind of decision? Another concerning case is cases in which an adversary might be using those prediction markets to inform their own tactics. And so it might change conflict itself. And even when there isn't any bad player, even when it's just people who are well-intentioned, I worry that many people thinking that there's going to be a war makes it much more likely for there to be a war.

48:40Because the other country interprets it as a threat and then they escalate and then we escalate in response and suddenly it's a spiral that nobody wants to happen. But our expectations shape the future.

48:55Big Technology Podcast Host:Why do you think people are so enthralled by these markets? I mean, they're having a moment because they've been accurate and I think better than the polls in some cases. but just the outsized attention and interest in them is very interesting. What do you think is behind? You're a philosopher. I'm a philosopher. So hit us. These are hypotheses. But one hypothesis is that we have truly become so accustomed to thinking in these betting terms that we are exporting that kind of mentality to more and more spheres of life. And I think that's a very bad thing. It also has to do with gamifying life. And there's something to me very disturbing about standing to earn money from a bet in which if you win, somebody is going to suffer greatly, like in the case of a war or something like that.

49:51Because you might say, well, prediction markets aren't that different to the stock market, right? It's also a kind of bet. But the stock market, when you invest in a company, you're actually contributing capital to that company in a way that is an important contribution to society. Whereas the prediction market is just a bet. And the only value they might have is if they're accurate, but accurate at what price and accurate in what sense and accurate when. And there's a lot of noise. And even if in one instance you might say, oh, in this case, the prediction markets were more accurate. Well, what does that mean?

50:24What does that really mean? And it doesn't nullify all of the other problems. We don't want to gamify everything. But I think maybe another reason why they're so popular is because there is this general sense of that we're living through times of high uncertainty. And that is, you know, leads people to be anxious. I can feel it as well. But I would like to invite people, when they feel that anxiety about uncertainty, to realize that uncertainty is actually good news because it means that the future is not written and that means that we can intercede in it, that we can influence it. And that is a great news.

51:03If you knew exactly what was going to happen tomorrow, you probably live in a police state.

51:07Big Technology Podcast Host:Yeah, but then, I mean, even if there's a prediction market out there, you could probably also intercede. I don't think you have to give up. Like, same thing with political polls, right? You could say the same thing about political polls as the prediction markets, that they become self-fulfilling prophecies because they do in many cases. Yeah. And why do we do political polls? In a way, we do it for entertainment. And is that good enough? Because I'm not sure it's good. Another reason might be, well, you might be well-informed. You might want to be well-informed because depending on how things are going, you might vote one way or another, right?

51:42Tactical voting. But I'm not sure we should incentivize people to be tactical voters. The ideal democracy, I think, is one in which people vote according to their conscience. And that says more about what they want. And that is more democratic, it seems to me, that when we push people to think tactically.

52:01Big Technology Podcast Host:Yeah. I don't think I'm going to stand on the table for political polls. Okay. Kind of annoy me also. Fair. All right. Let's end with this. I mean, you have a perspective that you've got to use comedy in this era. And that's sort of a counterweight to some of these ills that you see. Talk a little bit more about that. Yeah, it's really funny because my first book, Privacy is Power, is kind of gloomy in a way. Because at the time, everybody was so excited about tech and not seeing surveillance. And I felt that we needed a warning, more of a warning. But now it seems like we're in such a gloomy space in which so many people are making horrible predictions about the future.

52:42And I talk with my students and sometimes I don't know if young people can even imagine a bright world. And if they can't even imagine it, how are we going to get to that kind of bright future? That I wanted to emphasize the good things that we have. And two very good things that we have and are very important resources and tools are first, the analog world. Sometimes we forget about it. We are so dazzled by the digital that we forget the world of things, of your favorite coffee shop and your favorite bar and the people you love and your dog and the ecological world and trees and rivers and to ground ourselves there and cherish and protect that.

53:19But the second thing is humor. And humor is quite important, not only because it's a way to make life more fun and get through the hardest parts of life better, but it's also a very important tool in the toolkit of democracy. When you lose sense of humor, you're probably also losing some amount of freedom and democracy. And for example, Milan Kundera, the novelist, wrote a novel called The Joke, making exactly this point, given his experience with communism. And so the way that we – one way to confront all these gloomy predictions are first, noticing that they're predictions. predictions are not facts they can be defied and thinking okay is that the future i want and if not what am i going to do to create the future that i want to live in but secondly to treat it a little bit with less seriousness i'm not saying i'd be mean or anything but just like laugh a little bit about the absurdity of life and and humor is also a kind of intelligence it's a kind of noticing the absurd and noticing what's off.

54:27And one example I give in the book is that of Seinfeld, because it's also about curtailing predictions. When something's funny, it surprises you in a certain way. Part of what makes the joke funny is that you're expecting something and then you get something else and that makes it funny. And Seinfeld was brilliant at this, is brilliant at this. And the show is a very interesting case, because it's exactly the opposite of what an algorithm would select. So the show was incredibly unsuccessful as a pilot. Focus groups thought that it was weak, and people didn't like it. It wasn't what people wanted to watch.

55:05So if we had had algorithms back then selecting what people want to watch, Seinfeld would have not been one of those cases. But there was one executive in NBC who really believed in the show and championed it. And the first few seasons were a bit successful. It had like a niche following, but it was still small. And then it took off. And part of why it took off is because it changed people's sensibilities. It changed our sense of humor. And that's part of what great comedy or great art or great literature does to us. It makes us look at the world different. And when we use prediction too heavily, when we only predict what's going to be successful based on what has been successful in the past, we are missing out on those innovations that will make us look at the world anew and different.

55:54Big Technology Podcast Host:Yeah. And to your point, the one thing that LLMs do the worst is humor. They cannot make jokes. And it's because I think like, as you point out, they're just used to the average of averages and not throwing curveballs. Exactly. And also because there's no one there. There's no one being irreverent towards power. And part of comedy is that. It's like the court jester. What makes it so funny is that they are challenging the king in a way. And they are the king. And they are the king, yeah. The book is Prophecy, Prediction, Power, and the Fight for the Future from Ancient Oracle's 2.ai, Chris Leflese.

56:30Big Technology Podcast Host:So great to have you. Thank you for coming on the show. This was fun. Thank you so much for having me, Alex. It was great. Awesome. All right, everybody. Thank you so much for listening and watching. We'll see you next time on Big Technology Podcast.

56:55Parles-tu français? Hablas espanol? Parli italiano?

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