Speaker 1: Bloomberg Audio Studios, podcasts, radio news.
Speaker 2: Algorithms are everywhere. They determine the price you pay for
Speaker 2: your Uber, what gets fed to you on TikTok and Instagram,
Speaker 2: and even the prices you pay in the supermarket. Is
Speaker 2: all of this algorithmic impact helping or harming people? To
Speaker 2: answer that question, let's bring in Cass Sunstein. He is
Speaker 2: the author of a new book, Algorithmic Harm, Protecting People
Speaker 2: in the Age of Artificial Intelligence, co written with Orrin
Speaker 2: bar Gil. Cass is also a professor at Harvard Law
Speaker 2: School and is perhaps best known for his books on
Speaker 2: Star Wars and co authoring Nudge with Nobel Laureate Dick Taylor. So, pass,
Speaker 2: let's just jump right into this and start by defining
Speaker 2: what is algorithmic harm?
Speaker 3: Okay, So let's use Star Wars. So let's say the
Speaker 3: Jedi Knights use algorithms, and they give people things that
Speaker 3: fit with their tastes and interests and information and people
Speaker 3: get If they're interested in books on behavioral economics, that's
Speaker 3: what they get at a price that suits them. If
Speaker 3: they're interested in a book on Star Wars, that's what
Speaker 3: they get at a price.
Speaker 4: That suits them.
Speaker 3: The sith by contrast, take advantage with algorithms of the
Speaker 3: fact that some consumers lack information and some consumers suffer
Speaker 3: from behavioral biases. So we're going to focus on consumers first.
Speaker 3: If people don't know much, let's say about healthcare product,
Speaker 3: an algorithm might know that that they're likely not to
Speaker 3: know much and might say, we have a fantastic baldness
Speaker 3: cure for you. Here it goes, and people will be
Speaker 3: duped and exploited. So that's exploitation of absence of information.
Speaker 3: That's algorithmic harm. If people are super optimistic and they
Speaker 3: think that some new product is going to last forever,
Speaker 3: when it tends to break on first usage, then the
Speaker 3: algorithm can know those are unrealistically optimistic people and exploit
Speaker 3: their behavioral bias.
Speaker 2: So I referenced a few obvious areas where algorithms are
Speaker 2: taking place. Uber pricing is one the books you see
Speaker 2: on Amazon is algorithmically driven. Clearly, a lot of social
Speaker 2: media for better or WORSEUS algorithmically driven, and even things
Speaker 2: like the sort of music you like on Pandora. What
Speaker 2: are some of the less obvious examples of how algorithms
Speaker 2: are affecting consumers and regular people every day.
Speaker 3: Okay, So let's start with us straightforward once and then
Speaker 3: we'll get a little subtle. So straightforwardly, it might be
Speaker 3: that people are being asked to pay a price that
Speaker 3: suits their economic situation. So if you have a lot
Speaker 3: of money, the algorithm knows that maybe the price will
Speaker 3: be twice as much as it would be if you
Speaker 3: were less wealthy. That I think is basically okay. It
Speaker 3: leads to greater efficiency in the system. It's like rich
Speaker 3: people will pay more for the same product than poor people,
Speaker 3: and the algorithm is aware of that. So that's not
Speaker 3: that subtle, but it's important. Also, not that subtle is
Speaker 3: targeting people based on what's known about their particular tastes
Speaker 3: and preferences. Let's put wealth to one side, and so
Speaker 3: it's known that certain people are super interested in dogs,
Speaker 3: other people are interested in cats. And there we go,
Speaker 3: and all that is very straightforward's happening. If consumers are
Speaker 3: sophisticated and knowledgeable, that can be a great thing to
Speaker 3: make markets work better. If they aren't, it can be
Speaker 3: a terrible thing to make consumers get manipulated and hurt.
Speaker 3: Here's something a little more subtle. If an algorithm knows,
Speaker 3: for example, that you like Olivia Rodrigo, and I hope
Speaker 3: you do, because she's really good. Then there are going
Speaker 3: to be a lot of Olivia or Rodrigo songs that
Speaker 3: are going to be put into your system. And let's
Speaker 3: say no one's really like Olivia Rodrigo, but let's suppose
Speaker 3: there are others who are vaguely like her, and.
Speaker 4: You're going to hear a lot of that.
Speaker 3: Now that might seem not like algorithmic harm, that might
Speaker 3: seem like a triumph of freedom and markets, but it
Speaker 3: might mean that piece of people's tastes will calcify, and
Speaker 3: we're going to get very bulkanized culturally with respect to
Speaker 3: what people see in here. So they're going to be
Speaker 3: Olivia Orrigo people, and then they're going to be led
Speaker 3: Zeppelin people, and they're going to be Frank Sinatra people.
Speaker 3: And there was another singer called Bach. I guess I
Speaker 3: don't know much about him, but there's Bach, and there
Speaker 3: would be Bach people. And that's culturally damaging, and it's
Speaker 3: also damaging for the development of individual tastes and preferences.
Speaker 2: So let's put this into a little broader context than
Speaker 2: simply musical tastes and I like all of those, so
Speaker 2: I haven't become bulkanized yet. But when we look at
Speaker 2: consumption of news media, when we look at consumption of information,
Speaker 2: it really seems like the country has self divided itself
Speaker 2: into these happy little media bubbles that are either far
Speaker 2: left leaning or far right leaning, which are is kind
Speaker 2: of weird because I always learned the bulk of the
Speaker 2: country and the traditional bell curve, most people are somewhere
Speaker 2: in the middle. Hey, maybe they're center right or center left,
Speaker 2: but they're not out on the tails. How does the
Speaker 2: these algorithms affect our consumption of news and information?
Speaker 3: About fifteen twenty years ago, there is a lot of
Speaker 3: concern that through individual choices, people would create echo chambers
Speaker 3: in which they would live. And that's a fair concern
Speaker 3: and it has created a number of, let's say, challenges
Speaker 3: for self government and.
Speaker 4: Learning.
Speaker 3: What you're pointing to is also emphasized in the book,
Speaker 3: which is that algorithms can echo chamber you. An algorithm
Speaker 3: might say, you know, you're keenly interested in immigration, and
Speaker 3: you have this point of view, so boy, are.
Speaker 4: We going to funnel to you lots of information?
Speaker 3: Because clicks are money and you're going to be clicking,
Speaker 3: click and clickling click kicking. And that might be a
Speaker 3: very good thing from the standpoint of the seller, so
Speaker 3: to speak, or the user of the algorithm, but from
Speaker 3: the standpoint of view, it's not so fantastic. And from
Speaker 3: the standpoint of our society it's less than not so fantastic,
Speaker 3: because people will be living in algorithm driven universes that
Speaker 3: are very separate from one another, and they can end
Speaker 3: up not liking each.
Speaker 2: Other very much, even worse than not liking each other.
Speaker 2: Their view of the world aren't based on the same
Speaker 2: facts or the same reality. Everybody knows about Facebook and
Speaker 2: to a lesser degree, TikTok and Instagram and how it
Speaker 2: very much bulkanized people into things. And we've seen that
Speaker 2: in the world of media. You have Fox News over here,
Speaker 2: in MSNBC over there. How significant of a threat does
Speaker 2: algorithmic news feeds present to the country as a democracy,
Speaker 2: self regulating, self determined democracy.
Speaker 4: Really significant?
Speaker 3: And there's algorithms and then there's large language models, and
Speaker 3: they can both be used to create situations in which,
Speaker 3: let's say the people in some city let's call Los Angeles,
Speaker 3: are seeing stuff that creates a reality that's very different
Speaker 3: from the reality that people are saying, and let's say,
Speaker 3: boise Idaho. And that can be a real problem for
Speaker 3: understanding one another and also for mutual problem solving.
Speaker 2: So let's apply this a little bit more to consumers
Speaker 2: and markets. You describe two specific types of algorithmic discrimination.
Speaker 2: One is price discrimination and the other is quality discrimination.
Speaker 2: Why should we be aware of this distinction? Do they
Speaker 2: both deserve regulatory attention?
Speaker 3: So if there is price discrimination through algorithms in which
Speaker 3: different people get different offers depending on what the algorithm
Speaker 3: knows about their wealth and tastes, that's one thing, and
Speaker 3: it might be okay. People don't stand up and cheer
Speaker 3: and say hooray. But if people who have a lot
Speaker 3: of resources are given an offer that's not as let's
Speaker 3: say seductive as one that is given to people who
Speaker 3: don't have a lot of resources, just because the price
Speaker 3: is higher for the roots than the poor, that's okay.
Speaker 3: There's something efficient and market friendly about that. If it's
Speaker 3: the case that people who are let's say, not caring
Speaker 3: much about whether a tennis racket is going to break
Speaker 3: after multiple uses, and other people who think that tennis
Speaker 3: racket really has to be solid because I play every
Speaker 3: day and I'm going to play for the next five years.
Speaker 3: Then some people are given the let's say, immortal tennis racket,
Speaker 3: and other people are given the one that's more fragile.
Speaker 3: That's also okay, so long as we're dealing with people
Speaker 3: who have a level of sophistication. They know what they're
Speaker 3: getting and they know what they need. It's the case
Speaker 3: that for either pricing or for quality, the algorithm is
Speaker 3: aware of the fact that certain consumers are particularly likely
Speaker 3: not to have relevant information, then everything goes haywire. And
Speaker 3: if this isn't frightening enough, note that algorithms are an
Speaker 3: increasingly or an increasingly excellent position to know. This person
Speaker 3: with whom I'm dealing doesn't know a lot about whether
Speaker 3: products are going to last, and I can exploit that.
Speaker 3: Or this person is very focused on today and tomorrow
Speaker 3: and next year doesn't really matter. The person's present biased
Speaker 3: and I can exploit that. And that's something that can
Speaker 3: damage vulnerable consumers a lot, either with respect to quality
Speaker 3: or with respect to pricing.
Speaker 2: So let's flesh that out a little more. I'm very
Speaker 2: much aware that when Facebook sells ads, because I've been
Speaker 2: pitched these from Facebook. They could target an audience based
Speaker 2: on not just their likes and dislikes, but their geography,
Speaker 2: their search history, their credit score, their purchase history. Like,
Speaker 2: they know more about you than you know about yourself.
Speaker 2: It seems like we've created an opportunity for some potentially
Speaker 2: abusive behavior. Where is the line crossed from hey, we
Speaker 2: know that you like dogs and so we're going to
Speaker 2: market dog food to you to we know everything there
Speaker 2: is about you, and we're going to exploit your behavior
Speaker 2: biases and some of your emotional weaknesses.
Speaker 3: Okay, so suppose there's a population of Facebook users who
Speaker 3: are super well informed about food and really rational about food.
Speaker 3: So they particularly happen to be fond of sushi, and
Speaker 3: Facebook is going hard at them with respect to offers
Speaker 3: for sushi and so forth. Now let's suppose there's another population,
Speaker 3: which is they know what they like about food, but
Speaker 3: they have kind of hopes and false.
Speaker 4: Beliefs both about the effect of food on health.
Speaker 3: Then you can really market to them things that will
Speaker 3: lead to poor choices. And I've made a stark distinction
Speaker 3: between fully rational, which is kind of economic speak, and
Speaker 3: you know, imperfectly informed and behaviorally biased people also economic speak,
Speaker 3: but it's really intuitive. There's a radio show maybe This
Speaker 3: will Bring It Home, that I listen to when I
Speaker 3: drive into work, and there's a lot of marketing about
Speaker 3: a product that is supposed to relieve pain. And I
Speaker 3: don't want to criticize any producer of any product, but
Speaker 3: I have reason to believe that the relevant product doesn't
Speaker 3: help much. But the the station that is marketing this
Speaker 3: product to people, this pain relief product, must know that
Speaker 3: the audience is vulnerable to it.
Speaker 4: And they must know exactly how to get at them.
Speaker 3: And that's not in the interest of that's not going
Speaker 3: to make America great again.
Speaker 2: To say the very least. So we've been talking about algorithms,
Speaker 2: but obviously the subtext is artificial intelligence, which seems to
Speaker 2: be the natural extension and for the development of algos
Speaker 2: tell us how as AI becomes more sophisticated and pervasive,
Speaker 2: how is this going to impact our lives as employees,
Speaker 2: as consumers, as citizens.
Speaker 3: Chat GPT, chances are, knows a lot about everyone who
Speaker 3: uses set. So I actually asked chat GPT recently, I
Speaker 3: use it some not huge. I asked it to say
Speaker 3: some things about myself, and it said a few things
Speaker 3: that were kind of scarily precise about me based on
Speaker 3: some number dozens.
Speaker 4: Not one hundreds.
Speaker 3: I don't think of engagements with chatch ept. So large
Speaker 3: language models that track your prompts can know a lot
Speaker 3: about you, and if they're able also to know your name,
Speaker 3: they can, you know, instantly, basically learn a ton about
Speaker 3: you online, and we need to have privacy protections that
Speaker 3: are working there. Still, it's the case that AI broadly
Speaker 3: is able to use algorithms, and generative AI can go
Speaker 3: well beyond the algorithms we've gotten familiar with both to
Speaker 3: make the beauty of algorithmic engagement, that is, here's what
Speaker 3: you like, here's what you want, we're going to help you,
Speaker 3: and the ugliness of algorithms, here's how we can exploit
Speaker 3: you to get you to buy. And of course I'm
Speaker 3: thinking of investments too, so in your neck of the woods,
Speaker 3: it would be a child's play to get people super
Speaker 3: excited about investments, which AI knows the people with whom
Speaker 3: it's engaging are particularly susceptible to even though they're really dumb, engagements.
Speaker 2: Really really interesting. So since we're talking about investing, I
Speaker 2: can't help but bring up both AI and algorithms trying
Speaker 2: to increase so called market efficiency. And I always go
Speaker 2: back to Uber's surge pricing. As soon as it starts
Speaker 2: to rain, the prices go up in the city. It's
Speaker 2: obviously not an emergency. It's just an annoyance. However, we
Speaker 2: do see situations of price gouging after a storm, after
Speaker 2: a hurricane, people only have so many batteries and so
Speaker 2: much plywood, and they kind of crank up prices. How
Speaker 2: do we determine what is the line between something like
Speaker 2: surge pricing and something like, you know, abusive price gouging.
Speaker 3: Okay, so you're in a terrific area of behavioral economics.
Speaker 3: So we know that in circumstances in which let's say
Speaker 3: demand goes up high because everyone needs a shovel and
Speaker 3: it's a snowstorm, people are really mad if the prices
Speaker 3: go up, though it might be just a sensible market adjustment.
Speaker 3: So as a first approximation, if there's a spectacular need
Speaker 3: for something, let's say, shovels or umbrellas, the market inflation
Speaker 3: of the cost. While it's morally abhorrent to many and
Speaker 3: maybe in principle morally abhorrent from the standpoint of standard economics.
Speaker 4: It's okay. Now, if it's the case.
Speaker 3: That people under short term pressure from the fact that
Speaker 3: there's a lot of rain are especially vulnerable and some
Speaker 3: kind of emotionally intense state, so they'll pay kind of
Speaker 3: anything for an umbrella, then there's a behavioral bias which
Speaker 3: is motivating people's willingness to pay a lot more than
Speaker 3: the product is worth.
Speaker 2: So let's talk a little bit about disclosures and the
Speaker 2: sort of mandates that are required when we look across
Speaker 2: the pond, when we look at Europe, they're much more
Speaker 2: aggressive about protecting privacy and making sure big tech companies
Speaker 2: are disclosing all the things they have to disclose. How
Speaker 2: far behind is the US and that generally and are
Speaker 2: we behind when it comes to disclosures about algorithms or AI.
Speaker 3: I think we're behind them in the sense that we're
Speaker 3: less privacy focused. But it's not clear that that's bad,
Speaker 3: And even if it isn't good, it's not clear that
Speaker 3: it's terrible. I think neither Europe nor the US has
Speaker 3: put their regulatory finger on the actual problem. So let's
Speaker 3: take the problem of algorithms not figuring out what people want,
Speaker 3: but algorithms exploiting a lack of information or a behavioral
Speaker 3: bias to get people to buy things at prices that
Speaker 3: aren't good for them.
Speaker 4: That's a problem. It's in the.
Speaker 3: Same universe as fraud and deception, and the question is
Speaker 3: what are we going to do about it. A first
Speaker 3: line of defense is to try to ensure consumer protection,
Speaker 3: not through heavy handed regulation. I'm a long time University
Speaker 3: of Chicago person. I have in my DNA not liking
Speaker 3: heavy handed regulation, but through helping people to know what
Speaker 3: they're buying and helping people not to suffer from a
Speaker 3: behavioral bias such as, let's say, incomplete attention or unrealistic
Speaker 3: optimism when they're buying things. So these are standard consumer
Speaker 3: protection things which many of our agencies in the US,
Speaker 3: home grown America, they've done that, and that's good and
Speaker 3: we need more of that. So that's first line of defense.
Speaker 3: Second line of defense isn't to say, you know, privacy, privacy, privacy,
Speaker 3: though maybe that's a good song to sing. It's to
Speaker 3: say right to algorithmic transparency. So this is something which
Speaker 3: neither the US, nor Europe, nor Asia, nor South America
Speaker 3: nor Africa has been very advanced on so this is
Speaker 3: a coming thing where we need to know what the
Speaker 3: algorithms are doing. So it's public what's Amazon's algorithm doing?
Speaker 3: That would be good to know, and it shouldn't be
Speaker 3: the case that some efforts to ensure transparency invade Amazon's
Speaker 3: legitimate rights.
Speaker 2: Really really fascinating, thanks Cas. Anybody who is participating in
Speaker 2: the American economy and society, consumers, investors, even just regular
Speaker 2: readers of news, needs to be away of how algorithms
Speaker 2: are affecting what they see, the prices they pay, and
Speaker 2: the sort of information they're getting. So with a little
Speaker 2: bit of forethought and the book Algorithmic Harm, you can
Speaker 2: protect yourself from the worst aspects of algorithms. And AI
Speaker 2: I'm Barry Redults you're listening to Bloomberg's at the Money