Michael I. Jordan: evaluation

Transcript context

…You you spoke about this 3 layer model. So there was an example where, you know, you might have, you know, consumers and they might have their data and you you've got Google and then, you know, Google was using the data, the consumers are getting a service and then Google might sell the data over here. That's kind of like a traditional model. Let let's start with that. Okay. So those are really kind of like bore Adam kind of things. We're we're being scientists there. We're trying to say what's a minimal model that exhibits some of the behavior that we want to study here. So let's think about a data market because data is not just now something you analyze to build a big LLM, it's also something you would sell and buy and has value. And also there's privacy concerns about data. So let's put a little minimal model together where we could study that. And so 1 we've done is called, we call it a 3 layer data market. And it exists in the real world. This is an abstraction, but it's a real thing. You've got a user or multiple users coming into some platforms. The platforms provide a service like, imagine payment service. And as I use that, they get data from me. They learn about what kind of purchase I've made and so on. And they use that data to make their service better. That's a good little nice loop there. Problem is that rarely do they make enough money off of that service. They take a small cut that the merchants don't like to give them. So they have to do other things to to to stay in business. So typically, now, for a long time now, probably 20 years, they've been selling their data to third party data buyers. And these are not evil people just trying to ruin people's privacy. They're they're trying to do market research, learning what what would work and what what are people really doing. So behavioral studies. And so that that there is value to them. They pay for it. Alright? So, you know, Google doesn't need this because they created this artificial advertising market, which we could talk more about, that kind of super powered all this nonsense. But other companies like Mastercard, what do have to have to sell their data. So so now, it's a 3 layer thing. And and as soon as that third layer was introduced, the the equilibrium has to shift because the the user who's sending their data in just lost something. They lost a little bit of privacy. Some third party that I don't know anything about is getting data about me. And I can't just accept that, you know, but I can't walk away. Also, there's a stress on the system now. So in an effective economic system, what would happen is that the you wouldn't just wait for the regulator to come in, the government say, no, this can't be done. What you would do is that the platforms would say, well, we'll offer you a tunable level of differential privacy for some cost. Or we'll just say that this our company, I'm Google, I'll offer you level 0.3 and some other company says, well, I'll offer you level 0.7. Okay? So the user looks at that and says, ah, 0.7. That's that's better. I I really care about my privacy, I'll go there. That company then will get start start to get more data and their service will get even better. And, oop, you got a little nice little feedback loop there. But now the data buyers will look at the data from that person. ill get start start to get more data and their service will get even better. And, oop, you got a little nice little feedback loop there. But now the data buyers will look at the data from that person. 0.7 means more noise has been added to the data. It's less valuable to the data buyer. Data buyer will say, I'll I'll spend less I'll give you less money for that. I'll give more money to Google. And so now you can see there's conflicting tendencies here. The incentives are aligned but they're not optimal for everybody. And so now the mathematics is not just an optimization problem, mathematics is an equilibrium problem. But it's an equilibrium problem that involves statistical assertions, data and how much you can predict with this data and so on and so you quantify that with error bars and statistical predictions. So you put that all together in a big mathematical system and you can find the equilibrium as a function of various system parameters. So for example, is there a minimal level of privacy that regulators could require or not? Or, you know, is there some heterogeneous privacy budget? You know, etcetera etcetera. You can put in various those sorts sectors and now you you do a plot of how the equilibria moved. And the equilibria have overall utilities for all the 3 players summed up. That's the social welfare. You can ask how high is the social welfare of that equilibrium versus this 1 versus this 1. Another regular could look at that and say, well, I prefer this 1 because it's overall higher social welfare. And laws could be made at that level. Okay. So even though this is a toy little you know, a little toy model, it has the greediness that I'm very interested in, predictive models, data markets, but, money, incentives, and a real system that really is already kinda working, people aren't thinking about it very well, just like in the drug discovery domain. But if you take an economics point of view, you can make a lot you can make the system better.…

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