Barry Ritholtz (5:29)

So, so let's put this into a little broader context than simply musical tastes. And I like all of those. So I have, I haven't become balkanized yet. But when we look at consumption of news media, when we look at consumption of information, it really seems like the country has self divided itself into these happy little media bubbles that are either far left leaning or far right leaning. Which is kind of weird because I always learned the bulk of the country and the traditional bell curve, most people are somewhere in the middle. Hey, maybe they're center right or center left. But they're not out on the tails. How does these algorithms affect our consumption of news and information?

Cass Sunstein (6:22)

About 15, 20 years ago, there was a lot of concern that through individual choices people would create echo chambers in which they would live. And that's a fair concern. And it has created a number of, let's say, challenges for self government and, and learning. What you're pointing to is also emphasized in the book, which is that algorithms can echo chamber you. An algorithm might say, you know, you're keenly interested in immigration and you have this point of view. So, boy, are we going to funnel to you lots of information because clicks are money and you're going to be clicking, clicking, clicking, click, clicking. And that might be a very good thing from the standpoint of the seller, so to speak, or the user of the algorithm. But from the standpoint of you, it's not so fantastic. And from the standpoint of our society, it's less than not so fantastic. Because people will be living in algorithm driven universes that are very separate from one another and they can end up not liking each other very much.

Barry Ritholtz (7:31)

Even worse than not liking each other, their view of the world aren't based on the same facts or the same reality. We everybody knows about Facebook and to a lesser degree, TikTok and Instagram and how it very much balkanized people into things. And we've seen that in the world of media. You have Fox News over here and MSNBC over there. How significant of a threat does algorithmic news feeds present to the country as a democracy? Self, self regulating, self determined democracy.

Cass Sunstein (8:11)

Really significant. And there's algorithms and then there are large language models and they can both be used to create situations in which, let's say the people in some city, let's call it Los Angeles, are seeing stuff that creates a reality that's very different from the reality that people are saying. And let's say Boise, Idaho. And that can be a real problem for understanding one another and also for mutual problem solving.

Barry Ritholtz (8:41)

So let's apply this a little bit more to consumers and markets. You describe two specific types of algorithmic discrimination. One is price discrimination and the other is quality discrimination. Why should we be aware of this distinction? Do they both deserve regulatory attention?

Cass Sunstein (9:03)

So if there is price discrimination through algorithms in which different people get different offers depending on what the algorithm knows about their wealth and tastes, that's one thing. And it might be okay, people don't stand up and cheer and say hooray. But if people who have a lot of resources are given an offer that's not as, let's say, seductive, as one that is given to people who don't have a lot of resources just because the price is higher for the rich, that's the board that. That's okay. There's something efficient and market friendly about that. If it's the case that people who are, let's say, not caring much about whether a tennis racket is going to break after multiple uses, and other people who think the tennis racket really has to be solid because I play every day and I'm going to play for the next five years, then some people are given the, let's say, immortal tennis racket and other. Other people are given the one that's more fragile. That's also okay. So long as we're dealing with people who have a level of sophistication, they know what they're getting and they know what they need. If it's the case that for either pricing or for quality, the algorithm is aware of the fact that certain consumers are particularly likely not to have relevant information, then everything goes haywire. And if this isn't frightening enough, note that algorithms are an increasingly. Are in an increasingly excellent position to know. This person with whom I'm dealing doesn't know a lot about whether products are going to last and I can exploit that, or this person is very focused on today and tomorrow and next year doesn't really matter. The person's present biased, and I can exploit that. And that's something that can damage vulnerable consumers a lot, either with respect to quality or with respect to pricing.

Barry Ritholtz (11:03)

So, so let's, let's flesh that out a little more. I'm, I'm very much aware that when Facebook sells ads, because I've been pitched these from Facebook, they could target an audience based on not just their likes and dislikes, but their geography, their search history, their credit score, their purchase history. Like they know more about you than you know about yourself. It seems like we've created an opportunity for some potentially abusive behavior. Where is the line crossed from? Hey, we know that you like dogs, and so we're going to market dog food to you to. We know everything there is about you, and we're going to exploit your behavioral biases and some of your emotional weaknesses.

Cass Sunstein (11:57)

Okay, so suppose there's a population of Facebook users who are, you know, super well informed about food and really rational about food. So they particularly happen to be fond of sushi, and Facebook is going hard, hard at them with respect to offers for sushi and so forth. Now, let's suppose there's another population which is they know what they like about food, but they have kind of hopes and false beliefs both about the effect of food on health. Then you can really market to them things that will lead to poor choices. And I've made a stark distinction between fully rational, which is kind of economic speak, and, you know, imperfectly informed and behaviorally biased people. Also economic speak, but it's really intuitive. There's a radio show, maybe this will bring it home, that I listen to when I drive into work. And there's a lot of marketing about a product that is supposed to relieve pain. And I don't want to criticize any producer of any product, but I have reason to believe that the relevant product doesn't help much. But the station that is marketing this product to people, this pain relief product, must know that the audience is vulnerable to it and they must know exactly how to get at them. And that's not in the interest of. That's not going to make America great.

Barry Ritholtz (13:32)

Again, to say the very least. So we've been talking about algorithms, but obviously the subtext is artificial intelligence, which seems to be the natural extension and further development of algos. Tell us how, as AI becomes more sophisticated and pervasive, how is this going to impact our lives as employees, as consumers, as citizens?

Cass Sunstein (14:03)

ChatGPT, chances are, knows a lot about everyone who uses it. So I actually asked Chachi PT recently. I use it some, not hugely. I asked it to say some things about myself and it said a few things that were kind of scarily precise about me based on some number, dozens, not hundreds. I don't think of engagements with ChatGPT, so large language models that track your prompts can know a lot about you. And if they're able also to know your name, they can, you know, instantly basically learn a ton about you online. And we need to have privacy protections that are working there. Still, it's the case that AI broadly is able to use algorithms, and generative AI can go well beyond the algorithms we've gotten familiar with to both to make the beauty of algorithmic engagement. That is, here's what you like, here's what you want, we're going to help you. And the ugliness of algorithms, here's how we can exploit you to get you to buy things. And of course, I'm thinking of investments, too. So in your neck of the woods, it would be child's play to get people super excited about investments, which AI knows the people with whom it's engaging are particularly susceptible to, even though they're.

Barry Ritholtz (15:30)

Really dumb and engagements really, really interesting. So since we're talking about investing, I can't help but bring up both AI and algorithms trying to increase so called market efficiency. And I always go back to Uber's surge pricing. As soon as it starts to rain, the prices go up in the city. It's obviously not an emergency, it's just an annoyance. However, we do see situations of price gouging. After a storm, after a hurricane, people only have so many batteries and so much plywood and they kind of crank up prices. How do we determine what is the line between something like surge pricing and something like, you know, abusive price gouging?

Cass Sunstein (16:23)

Okay, so you're in a terrific area of behavioral economics. So we know that in circumstances in which, let's say demand goes up high because everyone needs a shovel and it's a snowstorm, people are really mad. If the prices go up, though, it might be just a sensible market adjustment. So as a first approximation, if there's a spectacular need for something, let's say shovels or umbrell, the market inflation of the cost. While it's morally abhorrent to many, and maybe in principle morally abhorrent, from the standpoint of standard economics, it's okay. Now if it's the case that people under short term pressure from the fact that there's a lot of rain are especially vulnerable. They're in some kind of emotionally intense state, so they'll pay kind of anything for an umbrella, then there's a behavioral bias which is motivating people's willingness to pay a lot more than the product is worth.

Barry Ritholtz (17:29)

So let's talk a little bit about disclosures and the sort of mandates that are required when we look across the pond, when we look at Europe, they're much more aggressive about protecting privacy and making sure big tech companies are disclosing all the things they have to disclose. How far behind is the US in that generally and are we behind when it comes to disclosures about algorithms or AI?

Cass Sunstein (17:57)

I think we're behind them in the sense that we're less privacy focused. But it's not clear that that's bad. And even if it isn't good, it's not clear that it's terrible. I think neither Europe nor the US has put their regulatory finger on the actual problem. So let's take the problem of algorithms not figuring out what people want, but algorithms exploiting a lack of information or a behavioral bias to get people to buy things at prices that aren't good for them. That's a problem it's in the same universe as fraud and deception. And the question is, what are we going to do about it? A first line of defense is to try to ensure consumer protection, not through heavy handed regulation. I'm a longtime University of Chicago person. I have in my DNA not liking heavy handed regulation, but through helping people to know what they're buying and helping people not to suffer from a behavioral bias such as, let's say, incomplete attention or unrealistic optimism when they're buying things. So these are standard consumer protection things which many of our agencies in the US homegrown made in America. They've done that, and that's good. And we need more of that. So that's first line of defense. Second line of defense isn't to say privacy, privacy, privacy, though maybe that's a good song to sing. It's to say right to algorithmic transparency. So this is something which neither the US nor Europe, nor Asia, nor South America, nor Africa has been very advanced on. So this is a coming thing where we need to know what the algorithms are doing so it's public. What's Amazon's algorithm doing? That would be good to know. And it shouldn't be the case that some efforts to ensure transparency invade Amazon's legitimate rights.

Barry Ritholtz (20:00)

Really, really fascinating. Thanks, Cas. Anybody who is participating in the American economy and society, consumers, investors, even just regular readers of news, needs to be aware of how algorithms are affecting what they see, the prices they pay, and the sort of information they're getting. So with a little bit of forethought and the book Algorithmic Harm, you can protect yourself from the worst aspects of algorithms and AI. I'm Barry Ritholtz, you're listening to Bloomberg's at the Money.

Unknown/Interjection (21:02)

Sam.