Transcript for Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning

You've dug deep into my history, I see. When deep. Yeah, I think what my favorite novel is Infinite Just by David Foster Wallace, which actually coincidentally much of it takes place in the halls of buildings right around us here at MIT. So that certainly had a big influence on me. And as you noticed, like when I was in high school, I actually even started college as an English major. So it was very influenced by sort of that genre of journalism at the time and thought I wanted to be a writer and then realize that an English major teaches you to read, but it doesn't teach you how to write. And then I became interested in math and computer science instead.

Yeah, I'd like to claim there was a deeper connection. But I think both Aaron and I kind of came at these topics first and foremost from a technical angle. I mean, you know, I'm kind of considered myself primarily an originally a machine learning researcher. And I think as we just watched like the rest of the society, the field technically advance. And then quickly on the heels of that kind of the buzz kill of all of the anti-social behavior by algorithms. just kind of realized there was an opportunity for us to do something about it from a research perspective. You know, a more to the point in your question, I mean, I do have an uncle who is literally a moral philosopher. And so in the early days of our technical work on fairness topics, I would occasionally, you know, run ideas behind him. So I mean, I remember an early email I sent to him in which I said, like, oh, You know, here's a specific definition of algorithmic fairness that we think is some sort of variant of roles in fairness. What do you think? And I thought I was asking a yes or no question, and I got back to your kind of classical philosopher's response. Well, it depends if you look at it this way, then you might conclude this. And that's when I realized that there was a real kind of rift between the ways philosophers and others had thought about things like fairness. from sort of a humanitarian perspective and the way that you needed to think about it as a computer scientist if you were going to kind of implement actual algorithmic solutions. But I would say

Yeah, I think that's right. I mean, I would even not go as far as you want to say that sort of the algorithmic work in these areas is solving the biggest problems. We discussed in the book the fact that really we are, there's a sense in which we're kind of looking where the light is in that You know, for example, if police are racist in who they decide to stop and frisk and that goes into the data, there's sort of no undoing that downstream by kind of clever algorithmic methods. And I think especially in fairness, I mean, I think less so in privacy where we feel like the community kind of really has settled on the right definition, which is differential privacy. If you just look at the algorithmic fairness that are at trial ready, you can see it's going to be much more of a mess. You've got these theorems saying, here are three entirely reasonable, desirable notions of fairness. And here's a proof that you cannot simultaneously have all three of them. So I think we know that algorithmic fairness compared to algorithmic privacy is going to be kind of a harder problem. And it will have to revisit, I think, things that have been thought about by many generations of scholars before us. So it's very early days for fairness, I think.

I mean, I'm an optimist. I tend to think that most people are good and want to do right and that deviations from that are, you know, kind of usually due to circumstance not to do to people being bad at heart.

Yeah, I do. I mean, my statement wasn't qualified to people not in positions of power. I mean, I think what happens in a lot of the cliche about absolute power corrupts absolutely. I mean, I think even short of that, having spent a lot of time on Wall Street, and also in arenas very, very different from Wall Street like academia. You know, one of the things I think I've benefited from by moving between two very different worlds is you become aware that, you know, these worlds kind of develop their own social norms and they develop their own rationales for, you know, behavior, for instance, that might look unusual to outsiders, but when you're in that world, it doesn't feel unusual at all. And I think this is true of a lot of, you know, professional cultures, for instance, and, you know, so then you're maybe slippery slope is too strong of a word, but you're in some world where you're mainly around other people with the same kind of viewpoints and training and world view as you. And I think that's more of a source of, you know, kind of abuses of power than sort of, you know, they're being good people and evil people. And it's somehow the evil people are the ones that somehow rise to power.

Yeah, I mean, or not the you drift, but even the things that don't make sense to the outside world don't seem unusual to you. So it's not sort of like a good or a bad thing, but like so for instance, you know, on in the world of finance, right, there's a lot of complicated types of activity that if you are not immersed in that world, you cannot see why the purpose of that, you know, that activity exists at all. It just seems like, you know, completely useless and people just like, you know, pushing money around. And when you're in that world, right? And you learn more, your view does become more nuanced, right? You realize, okay, there is actually a function to this activity. And for some cases, you would conclude that actually, if magically we could eradicate this activity tomorrow, it would come back because it actually is like serving some useful purpose. It's just a useful purpose. It's very difficult for outsiders to see. And so I think, you know, lots of professional work environments or cultures as I might put it, kind of have these social norms that, you know, don't make sense to the outside world. Academia is the same, right? I mean, lots of people look at academia and say, you know, what the hell are you people doing? Why are you paid so much in some cases at taxpayer expenses? to do, you know, to publishing papers and publishing reads, you know, but when you're in that world, you come to see the value for it and but even though you might not be able to explain it to, you know, the person in the street.

And finance, I think the primary example I would give is leverage, right? So being allowed to borrow to sort of use 10 times as much money as you've actually borrowed. So that's an example of something that before I had any experience in financial markets, I might have looked at and said, well, What is the purpose of that? That just seems very dangerous, and it is dangerous, and it has proven dangerous. But if the fact of the matter is that sort of on some particular time scale, you are holding positions that are very unlikely to lose, you know, your value at risk or variance is like one or five percent. then it kind of makes sense that you'd be allowed to use a little bit more than you have, because you have some confidence that you're not going to lose it all in a single day. Now, of course, when that happens, we've seen what happens, not too long ago, but the idea that it serves no useful economic purpose under any circumstances is definitely not true.

First of all, let me riff for a second on your specific analog and example. So because early in the book, when we're just trying to describe algorithms, period, we say like, okay, you know, what's an example of an algorithm? or an algorithm that problem first of all like it's sorting right you have a bunch of index cards with numbers on them and you want to sort them and we describe you know an algorithm that sweeps all the way through finds the smallest number puts it at the front then sweeps through again finds the second smallest number So we make the point that this is an algorithm, and it's also a bad algorithm, in the sense that it's quadratic rather than n log n, which we know is optimal for sorting. And we make the point that's sort of like, so even within the confines of a very precisely specified problem, There might be many, many different algorithms for the same problem with different properties. Some might be faster in terms of running time. Some might use less memory. Some might have better distributed implementations. And so the point is, is that already we're used to in computer science thinking about trade-offs between different types of quantities and resources. And they're being better and worse algorithms. And our book is about that part of algorithmic ethics that we know how to put on that same kind of quantitative footing right now. So just to say something that our book is not about, our book is not about kind of broad fuzzy notions of fairness. It's about very specific notions of fairness. There's more than one of them. There are tensions between them. But if you pick one of them, you can do something akin to saying that this album is 97% ethical. You can say, for instance, for this lending model, the false rejection rate on black people and white people is within 3%. So we might call that to 97% ethical algorithm and a 100% ethical algorithm would mean that that difference is 0%.

First of all, I didn't certainly didn't mean to give the impression that you can kind of measure memory speed trade-offs, and that there's a complete mapping from that onto fairness, for instance, or ethics and accuracy, for example. In the type of fairness definitions that are largely the objects of study today and starting to be deployed, You as the user of the definitions, you need to make some hard decisions before you even get to the point of designing fair algorithms. One of them, for instance, is deciding who is that you're worried about protecting, who you're worried about being harmed by, for instance, some notion of discrimination or unfairness. And then you need to also decide what constitutes harm. So for instance, in a lending application, maybe you decide that, you know, falsely rejecting a credit worthy individual. You know, sort of the false negative is the real harm and that false positives i.e. people that are not credit worthy or are not going to repay your loan that get a loan you might think of them as lucky. And so that's not a harm although it's not clear that if you are don't have the means to repay a loan that being given a loan is not also a harm. So, you know, the literature is sort of so far quite limited in that you sort of need to say, who do you want to protect and what would constitute harm to that group? And when you ask questions like, well, algorithms feel ethical one way in which they won't under the definitions that I'm describing is if, you know, if you are an individual who is falsely denied alone. incorrectly denied alone, all of these definitions basically say, well, your compensation is the knowledge that we are, we're also falsely denying loans to other people, other groups at the same rate that we're doing it to you. And so there is actually this interesting, even technical tension in the field right now, between these sort of group notions of fairness. and notions of fairness that might actually feel like real fairness to individuals. They might really feel like their particular interests are being protected or thought about by the algorithm rather than just the groups that they happen to be members of.

I mean, I think we're not even at the point where we can sensibly think about that. So first of all, you know, We're talking here both about fairness applied at the group level, which is a relatively weak thing, but it's better than nothing. And also the more ambitious thing of trying to give some individual promises. But even that doesn't incorporate, I think something that you're hinting at here is what a child might have called subjective fairness, right? So a lot of the definitions, I mean, all of the definitions in the algorithmic fairness literature are what I would kind of call received wisdom definitions. It's sort of, you know, somebody like me sits around and thinks like, okay, you know, I think here's a technical definition of fairness that I think people should want or that they should, you know, think of as some notion of fairness, maybe not the only one, maybe not the best one, maybe not the last one. But we really actually don't know from a subjective standpoint, like what people really think is fair. We just started doing a little bit of work in our group at actually doing human subject experiments in which we, you know, ask people about, you know, we ask them questions about fairness, we survey them, we, you know, we show them pairs of individuals, and let's say a criminal recidivism prediction setting, and we ask them, do you think these two individuals should be treated the same as a matter of fairness? To my knowledge, there's not a large literature in which ordinary people are asked about, you know, they have sort of notions of their subjective fairness elicited from them. It's mainly, you know, kind of scholars who think about fairness, you know, kind of making up their own definitions. And I think I think this needs to change actually for many social norms not just for fairness, right? So there's a lot of discussion these days in the AI community about interpretable AI or understandable AI. And as far as I can tell, Everybody agrees that deep learning or at least the outputs of deep learning are not very understandable and people might agree that sparse linear models with integer coefficients are more understandable. But nobody's really asked people. There's very little literature on showing people models and asking them do they understand what the model is doing. And I think that in all these topics, as these fields mature, we need to start doing more behavioral work.

Yeah, I'd like to think that what you say is true about computer scientists and psychology from my own limited, wandering into human subject experiments, we have a great deal to learn. Not as computer science, but AI and machine learning more specifically, I kind of think of as imperialist research communities in that kind of like physicists in an earlier generation. computer scientists kind of don't think of any scientific topic as off limits to them they will like freely wander into areas that others have been thinking about for decades or longer and you know we usually tend to embarrass ourselves in those efforts for for some amount of time like you know I think reinforcement learning is a good example right so a lot of the early work in reinforcement learning I have complete sympathy for the control theorist that looked at this and said, OK, you are reinventing stuff that we've known since like the 40s, right? But in my view, eventually this computer scientists have made significant contributions to that field, even though we kind of embarrassed ourselves for the first decade. So I think if computer scientists are going to start engaging in kind of psychology, human subjects, type of research, we should expect to be embarrassing ourselves for a good 10 years or so, and then hope that it turns out as well as some other areas that we've waited into.

So let me start by answering a very good high-level question with a slightly narrow technical response, which is these group definitions of fairness, like here's a few groups, like different racial groups, maybe gender groups, maybe age, what have you. And let's make sure that for none of these groups, we have a false negative rate, which is much higher than any other one of these groups. So these are kind of classic group aggregate notions of fairness. But at the end of the day, an individual, you can think of as a combination of all of their attributes. They're a member of a racial group. They have a gender They have an age, you know, and many other, you know, demographic properties that are not biological, but that, you know, are still, you know, very strong determinants of outcome and personality in the light. So one, I think useful spectrum is to sort of think about that array between the group and the specific individual and to realize that in some ways asking for fairness at the individual level is to sort of ask for group fairness simultaneously for all possible combinations of groups. So in particular, in particular, yes. if I build a predictive model that meets some definition of fairness by race, by gender, by age, by what have you marginally, to get it slightly technical, it's sort of independently. I shouldn't expect that model to not discriminate against disabled, Hispanic, women over age 55, making less than $50,000 a year annually, even though I might have protected each one of those attributes marginally.

So at the end of the day, we could think of all of ourselves as groups of size one, because eventually there's some attribute that separates you from me and everybody from everybody else in the world. And so It is possible to put these incredibly coarse ways of thinking about fairness and these very, very individualistic specific ways on a common scale. And one of the things we've worked on from a research perspective is, so we know how to You know, in relative terms, we know how to provide fairness guarantees at the course of the end of the scale. We don't know how to provide kind of sensible, tractable, realistic fairness guarantees at the individual level, but maybe we could start creeping towards that by dealing with more refined subgroups. I mean, we gave a name to this phenomenon where you know, you protect you enforce some additional definition of fairness for a bunch of marginal attributes or features, but then you find yourself discriminating against a combination of them. We call that fairness gerrymandering, because like political gerrymandering, you're giving some guarantee at the aggregate level, but when you kind of look in a more granular way at what's going on, you realize you're achieving that aggregate guarantee by sort of favoring some groups and discriminating against other ones. And so there are, you know, it's early days, but there are algorithmic approaches that let you start creeping towards that, you know, individual and a spectrum.

Is that kind of what humans, I mean of course, you know, I don't need to tell you that of course technically one could incorporates such weights if you wanted to into a definition of fairness. You know, fairness isn't interesting topic in that having worked in the book being about both fairness, privacy and many other social norms. Fairness of course is a much, much more loaded topic. So privacy, people want privacy, people don't like violations of privacy, violations of privacy, cause damage, angst, and bad publicity for the companies that are victims of them. But sort of everybody agrees, more data privacy would be better than less data privacy. And you don't have these, somehow, the discussions of fairness don't become politicized along other dimensions like race and about gender and, you know, whether we, you know, it, you quickly find yourself kind of revisiting topics that have been kind of unresolved forever, like affirmative action, right? So, you know, like, why are you protecting? Some people will say, why are you protecting this particular racial group? And others will say, well, we need to do that as a matter of retribution. Other people will say it's a matter of economic opportunity. I don't know which of, you know, whether any of these are the right answers, but you sort of fairness is sort of special. And as soon as you start talking about it, you inevitably have to participate in debates about fair to whom at what expense to who else. I mean, even in criminal justice, you know, where people talk about fairness in criminal sentencing or predicting failures to appear or making parole decisions or the like, they'll point out that these definitions of fairness are all about fairness for the criminals and what about fairness for the victims? When I basically say something like, well, The false incarceration rate for black people and white people needs to be roughly the same. There's no mention of potential victims of criminals in such a fairness definition.

Yeah, yeah, I agree. And so it's interesting to be a researcher trying to do for the most part technical algorithmic work. But Aaron and I both quickly learned you cannot do that and then go out and talk about it and expect people to take it seriously. If you're unwilling to engage in these broader debates that are entirely extra algorithmic, right? They're not about You know, algorithms are making algorithms better. They're sort of, you know, as you said, sort of like what should society be protecting in the first place?

Really not touch that. We want to touch that and we do touch it. Just to make sure I'm not promising your viewers more than we know how to provide. If you pick a definition of fairness, I'm worried about gender discrimination. You pick a notion of harm like false rejection for a loan, for example. You give me a model. I can definitely first of all go audit that model. It's easy for me to go from data. to say like, okay, your false rejection rate on women is this much higher than it is on men, okay? But once you also put the fairness into your objective function, I mean, I think the table that you're talking about is what we would call the Pareto curve, right? You can literally trace out And we give examples of such plots on real data sets in the book. You have two axes. On the x-axis is your error. On the y-axis is unfairness by whatever if it's like the disparity between false rejection rates between two groups. And, you know, your algorithm now has a knob that basically says, how strongly do I want to enforce fairness? And the less unfairness, you know, if the two axes are air and unfairness, we'd like to be at zero zero. We'd like zero air and zero unfairness simultaneously. Anybody who works in machine learning knows that you're generally not going to get to zero error period without any fairness constraint whatsoever. So that's not going to happen. But in general, you'll get this, you'll get some kind of convex curve that specifies the numerical trade-off you face. If I want to go from 17% error down to 16% error, what will be the increase in unfairness that I'm experienced as a result of that? And so this curve kind of specifies the, you know, kind of undominated models, models that are off that curve are, you know, can be strictly improved in one or both dimensions. You can, you know, either make the error better or the unfairness better or both. And I think our view is that not only are these objects, these Pareto curves, you know, efficient frontiers, as you might call them, not only are they valuable scientific objects, I actually think that they in the near term might need to be the interface between researchers working in the field and stakeholders in given problems. So, you know, you could really imagine telling a criminal jurisdiction look, if you're concerned about racial fairness, but you're also concerned about accuracy. You want to release on parole, people that are not going to recommit a violent crime, and you don't want to release the ones who are. So that's accuracy. But if you also care about those, the mistakes you make not being disproportionately on one racial group or another, You can show this curve. I'm hoping that in the near future, it'll be possible to explain these curves to non-technical people that are the ones that have to make the decision, where do we want to be on this curve? What are the relative merits or value of having lower air versus lower unfairness? You know, that's not something computer scientists should be deciding for society, right? That, you know, the people in the field, so to speak, the policy makers, the regulators, that's who should be making these decisions. But I think and hope that they can be made to understand that these tradeoffs generally exist and that You need to pick a point and ignoring the trade-off, you know, you're implicitly picking a point anyway. Right. Right. You just don't know it and you're not admitting it.

The specific trade I talked about just in order to make things very concrete was between numerical error and some numerical measure of unfairness.

Like, you know, the probability or frequency with which you release somebody on parole who then goes on to recommit a violent crime or keep incarcerated somebody who would not have recommitted a violent crime.

And then the fairness axis might be the difference between racial groups in the kind of false, false positive predictions, namely people that I kept incarcerated, predicting that they would recommit a violent crime when in fact they wouldn't have.

And then we, this also quickly, you know, bleeds into questions like, well, maybe if one group really does recommit crimes at a higher rate, The reason for that is that at some earlier point in the pipeline or earlier in their lives, they didn't receive the same resources that the other group did. And so there's always in kind of fairness discussions, the possibility that the real injustice came earlier, earlier in this individual's life, earlier in this group's history, et cetera, et cetera.

By some definitions of fairness or some theories of fairness, yeah, others would say like look, it's not to correct that injustice, it's just to kind of level the playing field right now and non-incorcerate, falsely incarcerated more people of one group than another group. But I mean, I think it might be helpful just to demystify a little bit about the many ways in which bias or unfairness can come into. algorithms especially in the machine learning here. Many of your viewers have probably heard these examples before. But let's say I'm building a face recognition system. And so gathering lots of images of faces and trying to train the system to recognize new faces of those individuals from training on training set of those faces of individuals. And, you know, it shouldn't surprise anybody, or certainly not anybody in the field of machine learning, if my training dataset was primarily white males. And I'm training that the model to maximize the overall accuracy on my training dataset. that, you know, the model can reduce its error most by getting things right on the white males, the constitute, the majority of the data said, even if that means that on other groups, they will be less accurate. Okay. Now, there's a bunch of ways you could think about addressing this. One is to deliberately put into the objective of the algorithm not to and not to optimize the error at the expense of this discrimination. And then you're going to back in the land of these kind of two-dimensional numerical trade-offs. a valid counter argument is to say like, well, no, you don't have to, there's no, you know, the notion of the tension between air and accuracy is a false one. You could instead just go out and get much more data on these other groups that are in the minority and, you know, equalize your data set or you could train a separate model on those subgroups and, you know, have multiple models. The point that we would, you know, we try to make in the book is that those things have cost too, right? Going out and gathering more data on groups that are relatively rare, compared to your plurality or majority group, that, you know, it may not cost you in the accuracy of the model, but it's going to cost, you know, it's going to cost the company developing this model more money to develop that. And it also costs more money to build separate predictive models and to implement and deploy them. So even if you can find a way to avoid the tension between error and accuracy and training of model, you might push the cost somewhere else, like money, like development time, research time and the like.

Yeah, I mean, for the most part in the book, you know, a point that we try to take some pains to make is that we don't view ourselves or people like us as being in the position of deciding for society, what the right social norms are, what the right definitions of fairness are. Our main point is to just show that If society or the relevant stakeholders in a particular domain can come to agreement on those sorts of things, there's a way of encoding that into algorithms in many cases, not in all cases. One other misconception that hopefully we definitely dispel is sometimes people read the title of the book and I think not unnaturally fear that what we're suggesting is that the algorithms themselves should decide what those social norms are and develop their own notions of fairness and privacy or ethics and we're definitely not suggesting that the title of the book is ethical algorithm by the way and I didn't think of that interpretation of the title that's interesting yeah yeah I mean, especially these days where people are concerned about the robots becoming our overlords, the idea that the robots would also develop their own social norms, just one step away from that. But I do think, you know, obviously, despite this claim or that people like us shouldn't be making those decisions for society, we are kind of living in a world where in many ways computer scientists have made some decisions that have fundamentally changed the nature of our society and democracy and sort of civil discourse and deliberation in ways that I think most people generally feel are bad these days, right?

Look I don't I'd never meet to be an apologist for the tech industry but I think it's it's a little bit too far to sort of say that explicit decisions were made about these things. So let's, for instance, take social media platforms, right? So like many inventions and technology and computer science, a lot of these platforms that we now use regularly, kind of started as curiosity, right? I remember when things like Facebook came out and it's predecessors like friends, or which nobody even remembers now. People really wonder, like, why would anybody want to spend time doing that? Even the web, when it first came out, when it wasn't populated with much content and it was largely hobbyists building their own brown shackle websites. A lot of people looked at this as, like, what is the purpose of this thing? Why is this interesting who would want to do this? And so even things like Facebook and Twitter, yes, technical decisions were made by engineers, by scientists, by executives in the design of those platforms. But I don't think 10 years ago anyone anticipated that those platforms, for instance, might kind of acquire undo, you know, influence on political discourse or on the outcomes of elections. And I think the scrutiny that these companies are getting now is entirely appropriate, but I think it's a little too harsh to kind of look at history and sort of say like, oh, you should have been able to anticipate that this would happen with your platform. And in this sort of gaming chapter of the book, one of the points we're making is that You know, these platforms right, they don't operate in isolation. So like unlike the other topics we're discussing like fairness and privacy, like those are really cases where algorithms can operate on your data and make decisions about you and you're not even aware of it. Okay. Things like Facebook and Twitter, these are systems, these are social systems, and their evolution, even their technical evolution because machine learning is involved, is driven in no small part by the behavior of the users themselves and how the users decide to adopt them and how to use them. I'm kind of like who really knew that until we saw it happen, who knew that these things might be able to influence the outcome of elections, who knew that they might polarize political discourse because of the ability to decide who you interact with on the platform and also with the platform naturally using machine learning to optimize for your own interest that they would further isolate us from each other. and feed us all basically just the stuff that we already agreed with. And so I think we've come to that outcome, I think, largely, but I think it's something that we all learned together, including the companies as these things happen. You asked, well, are there algorithmic remedies to these kinds of things? And again, these are big problems that are not going to be solved with, somebody going in and changing a few lines of code somewhere in a social media platform. But I do think in many ways there are definitely ways of making things better. I mean, like an obvious recommendation that we make at some point in the book is like, look, you know, to the extent that we think that machine learning applied for personalization purposes in things like news feed, you know, or other platforms has led to polarization and intolerance of opposing viewpoints. As you know, right, these algorithms have models, right? And they kind of place people in some kind of metric space. And they place content in that space. And they sort of know the extent to which I have an affinity for a particular type of content. And by the same token, they also probably have that same model probably gives you a good idea of the stuff I'm likely to violently disagree with or be offended by. So, you know, in this case, there really is some knob you could tune. It says like, instead of showing people only what they like and what they want, let's show them some stuff that we think that they don't like or that's a little bit further away. And you could even imagine users being able to control this, you know, just like everybody gets a slider. And that slider says, like, you know, how much stuff do you want to see that's kind of, you know, you might disagree with or at least further from your interest. It's almost like an exploration button.

Well, I mean, optimizing for engagement kind of got us where we are.

I mean, it's definitely possible to do different, right? And again, you know, it's not as if I think that doing something different than optimizing for engagement won't cost these companies in real ways, including revenue and profitability potentially. Sure term at least. Yeah, in the short term, right? And again, you know, if I worked at these companies, I'm sure that it It would have seemed like the most natural thing in the world also to want to optimize engagement. And that's good for users in some sense. You want them to be vested in the platform and enjoying it and finding it useful, interesting, and or productive. But my point is that the idea that there's that it's sort of out of their hands, as you said, or that there's nothing to do about it, Never say never, but that strikes me as implausible as a machine learning person. These companies are driven by machine learning and this optimization of engagement is essentially driven by machine learning. It's driven by not just machine learning, but very, very large-scale AB experimentation where you have to tweak some element of the user interface or tweak some component of an algorithm or tweak some component or feature of your click-through prediction model. And my point is that anytime you know how to optimize for something, you, you know, by deaf, almost by definition, that solution tells you how not to optimize for it or to do something different. Engagement can be measured.

That's right. So I'm not claiming that doing something different will immediately make it apparent that this is a good thing for society. In particular, I mean, I think one way of thinking about where we are on some of these social media platforms is that, you know, It kind of feels a bit like we're in a bad equilibrium, right? That these systems are helping us all kind of optimize something biopically and selfishly for ourselves. And of course, from an individual standpoint at any given moment, like, why would I want to see things in my news feed that I found irrelevant offensive, or, you know, or the like, okay? But, you know, maybe by all of us, you know, having these platforms, my opically optimized in our interests, we have reached a collective outcome as a society that we're unhappy with in different ways. Let's say, with respect to things like, you know, political discourse and tolerance of opposing viewpoints.

I think no thanks would be the first response, but there are many aspects of being the head of the entire company that are kind of entirely exogenous to many of the things that we're discussing here. And so I don't really think I would need to be CEO at Facebook to kind of implement the more limited set of solutions that I might imagine. But I think one One concrete thing they could do is they could experiment with letting people who chose to see more stuff in their news feed that is not entirely kind of chosen to optimize for their particular interests, beliefs, et cetera.

Or that same person could say that will be long-term healthy for the platform. for the platform's influence on society outside of the platform. It's easy for me to sit here and say these things, but conceptually I do not think that these are totally or shouldn't be completely alien ideas. You could try things like this and it wouldn't be, you know, we wouldn't have to invent entirely new science to do it because if we're already embedded in some metric space and there's a notion of distance between you and me and every other, every piece of content, then, you know, we know exactly, you know, the same model that tells, you know, dictates how to make me really happy, also tells how to make me as unhappy as possible as well.

I haven't thought about that question specifically in the context of fairness. I definitely would agree with that statement in the large. I am one of many machine learning researchers who do believe that the great successes that have been shown in machine learning recently are great successes. But they're on a pretty narrow set of tasks. I don't think we're kind of notably closer to general artificial intelligence now than we were when I started my career. I mean, there's been progress. And I do think that we are kind of as a community maybe looking a bit where the light is, but the light is shining pretty bright there right now and we're finding a lot of stuff. So I don't want to argue with the progress that's been made in areas like deep learning, for example.

Like what should be fair? What should be fair? The more you know about a field, the more aware you are of its limitations. And so I'm pretty leery of sort of trying, you know, there's so much we don't all, we already don't know in fairness, even when we're the ones picking the fairness definitions and comparing alternatives and thinking about the tensions between different definitions. that the idea of kind of letting the algorithm start exploring as well. I definitely think, you know, this is a much narrower statement. I definitely think that kind of algorithmic auditing for different types of unfairness, right? So like in this gerrymandering example where I might want to prevent not just discrimination against very broad categories, but against combinations of broad categories, you know, you quickly get to a point where there's a lot of categories, there's a lot of combinations of end features, and you know, you can use algorithmic techniques to sort of try to find the subgroups on which you're discriminating the most and try to fix that. That's actually kind of the form of one of the algorithms we developed for this fairness, gerrymandering problem. But I'm, you know, partly because of our sort of our scientific ignorance on these topics right now, and also partly just because these topics are so loaded emotionally for people that I just don't see the value. I mean, again, never say never, but I just don't think we're at a moment where it's a great time for computer scientists to be rolling out the idea like, hey, You know, you know, not only have we kind of figured fairness out, but, you know, we think the algorithm should start deciding what's fair or giving input on that decision. I just don't like the cost benefit analysis to the field of kind of going there right now just doesn't seem worth it to me.

I was about to agree with everything you said except that last point. I think that the other way of looking at it is that I think computer scientists, and many of us are. but we need to wait out into the world more, right? I mean, just the influence that computer science and therefore computer scientists have had on society at large, just like, has exponentially magnified in the last 10 or 20 years or so. And, you know, before when we were just tinkering around amongst ourselves and it didn't matter that much, there was no need for sort of computer scientists to be citizens of the world more broadly. And I think those days need to be over very, very fast. And I'm not saying everybody needs to do it. But to me, the right way of doing it is to not to sort of think that everybody else is going to become a computer scientist. But I think people are becoming more sophisticated about computer science, even lay people. One of the reasons we decided to write this book as we thought ten years ago I wouldn't have tried this because I just didn't think that sort of people's awareness of algorithms and machine learning you know, the general population would have been high and I mean, you would have had to first, you know, write one of the many books kind of just explicating that topic to a lay audience first. Now I think we're at the point where like lots of people without any technical training at all know enough about albums machine learning that you can start getting to these nuances of things like ethical algorithms. I think we agree that there needs to be much more mixing, but I think a lot of the onus of that mixing needs to be on the computer science community.

Yeah, I mean I don't think I disagree enough, but I think that that trend of more and more people and more and more disciplines adopting ideas from computer science, learning how to code. I think that that trend seems firmly underway. I mean, you know, like an interesting, digressive question along these lines is maybe in 50 years, there won't be computer science departments anymore because the field will just sort of be ambient in all of the different disciplines. And you know, people will look back and You know, having a computer science department will look like having an electricity department or something. It's like, you know, everybody uses this. It's just out there. I mean, I do think there will always be that kind of canoe style core too. But it's not an implausible path that we kind of get to the point where the academic discipline of computer science becomes somewhat marginalized because of its very success in kind of infiltrating all of science and society and the humanities, et cetera. what is differential privacy or more broadly algorithmic privacy algorithmic privacy more broadly is just the study or the notion of privacy definitions or norms being encoded inside of algorithms and so you know I think we count among this body of work, just the literature and practice of things like data and anonymization, which we kind of at the beginning of our discussion of privacy, say like, okay, this is sort of a notion of algorithmic privacy. It kind of tells you something to go do with data. But our view is that it's, and I think this is now quite widespread, that it's, despite the fact that those notions of anonymization, kind of redacting and coarsening are the most widely adopted technical solutions for data privacy. They are like deeply fundamentally flawed. And so, to your first question, what is differential privacy? differential privacy seems to be a much, much better notion of privacy that kind of avoids a lot of the weaknesses of an automation notions while still letting us do useful stuff with data. What's an automation of data? So by an automation, I'm kind of referring to techniques like I have a database. the rows of that database are let's say individual people's medical records. I want to let people use that data, maybe I want to let researchers access that data to build predictive models for some disease, but I'm worried that that will leak you know, sensitive information about specific people's medical records. So anonymization broadly refers to the set of techniques where I say, like, okay, I'm first going to like, like, I'm going to delete the column with people's names. I'm going to not put, so that would be like a redaction. I'm just redacting that information. I am going to take ages and I'm not going to say you're exact age. I'm going to say whether you're 0 to 10, 10 to 28, 20 to 30. I might put the first three digits of your zip code, but not the last two, et cetera, et cetera. And so the idea is that through some series of operations like this on the data, I anonymize it, you know, another term of art that's used is removing personally identifiable information. And, you know, this is basically the most common way of providing data privacy, but that's in a way that still lets people access the some variant form of the data.

So, this is exactly the problem with these notions, right? Is that notions of a non-monization, removing personally identifiable information, the fundamental conceptual flaw, is that You know, these definitions kind of pretend as if the data set in question is the only data set that exists in the world or that ever will exist in the future. And of course, things like the Netflix prize and many, many other examples since the Netflix prize, I think that was one of the earliest ones though. You can re-identify people that were anonymized in the data set by taking that anonymized data set and combining it with other allegedly anonymized data sets and maybe publicly available information about you.

And from that combined with other things, I think foreign posts and so on, you can... Yeah, that case it was specifically the internet movie database, where lots of Netflix users publicly rate their movie preferences. And so the anonymized data and Netflix, it's just this phenomenon. I think that we've all come to realize in the last decade or so is that just knowing a few apparently irrelevant innocuous things about you can often act as a fingerprint. Like if I know, you know, What rating you gave to these 10 movies and the date on which you entered these movies? This is almost like a fingerprint for you and the sea of all Netflix users. We're just another paper on this in science or nature about a month ago that 18 attributes. My favorite example of this was actually a paper from several years ago now where it was shown that just from your likes on Facebook, just from the, you know, the things on which you clicked on the thumbs up button on the platform. Not using any information, demographic information, nothing about who your friends are just knowing the content that you had liked. was enough to, you know, in the aggregate accurately predict things like sexual orientation, drug and alcohol use, whether you were the child of divorced parents. So we live in this era where, you know, even the apparently irrelevant data that we offer about ourselves on public platforms and forums, often unbeknownst to us, more or less acts as signature or, you know, fingerprint. and that if you can kind of do a join between that kind of data and allegedly anonymize data, you have real trouble.

So there is differential privacy. So differential privacy basically is an alternate much stronger notion of privacy than these anonymization ideas. And it's a technical definition, but the spirit of it is we compare to alternate worlds. So let's suppose I'm a researcher and I want to do, there's a database of medical records and one of them is yours. And I want to use that database of medical records to build a predictive model for some disease. So based on people's symptoms and test results and the like, I want to build a model predicting the probability that people are disease. So this is the type of scientific research that we would like to be allowed to continue. And in differential privacy, you ask a very particular counterfactual question. We basically compare two alternatives. One is when I do this, I build this model on the database of medical records, including your medical record. And the other one is where I do the same exercise with the same database with just your medical record removed. So basically, it's two databases, one with n records in it, and one with n minus 1 records in it. The n minus 1 records are the same, and the only one that's missing in the second case is your medical record. So differential privacy basically says that any harms that might come to you from the analysis in which your data was included are essentially nearly identical to the harms that would have come to you if the same analysis had done been done without your medical record included. So in other words, this doesn't say that bad things cannot happen to you as a result of data analysis. It just says that these bad things were going to happen to you already, even if your data wasn't included. And to give a very concrete example, right? You know, like we discussed at some length, the study that, you know, in the 50s that was done that created the established the link between smoking and lung cancer. And we make the point that like, well, if your data was used in that analysis, And, you know, the world kind of knew that you were a smoker because, you know, there was no stigma associated with smoking before that those findings. Real harm might have come to you as a result of that study that your data was included in, in particular, you're insure or now might have a higher posterior belief that you might have lung cancer and raise your premium. So you've suffered economic damage. But the point is, is that If the same analysis has been done without with all the other n minus 1 medical records and just years missing, the outcome would have been the same. Your data was an idiocyncratically crucial to establishing the link between smoking and lung cancer because the link between smoking and lung cancer is like a fact about the world that can be discovered with any sufficiently large database of medical records.

Yeah. So it's basically by adding noise to computations. So the basic idea is that Every differentially private algorithm, first of all, or every good, differentially private algorithm, every useful one is a probabilistic algorithm. So it doesn't on a given input. If you gave the algorithm the same input multiple times, it would give different outputs each time from some distribution. And the way you achieve differential privacy algorithmically is by kind of carefully and tastefully adding noise to a computation in the right places. And to give a very concrete example, if I want to compute the average of a set of numbers, the non-private way of doing that is to take those numbers and average them and release a numerically precise value for the average. in differential privacy, you wouldn't do that. You would first compute that average to numerical precision, and then you'd add some noise to it. You'd add some kind of zero mean, Gaussian, or exponential noise to it, so that the actual value you output is not the exact mean, but it'll be close to the mean. But the noise that you add will sort of prove that nobody can kind of reverse engineer any particular value that went into the average.

Yeah, so I'm a relatively recent member of the differential privacy community. My co-author Aaron Roth is, you know, really one of the founders of the field and has done a great deal of work. I've learned a tremendous amount working with him on it. It's a pretty good growing up field already. Yeah, but it now is pretty mature. But I must admit, the first time I saw the definition of differential privacy my reaction was like, well, that is a clever definition and it's really making very strong promises and my, you know, You know, I first saw the definition in much earlier days, and my first reaction was like, well, my worry about this definition would be that it's a great definition of privacy, but that it'll be so restrictive that we won't really be able to use it. Like, you know, we won't be able to do compute many things in a differentially private way. So that's one of the great successes of the field, I think, is ensuring that the opposite is true, and that, you know, most things that we know how to compute absent any privacy considerations can be computed in a differentially private way. So for example, pretty much all of statistics and machine learning can be done differently. So pick your favorite machine learning algorithm, back propagation in neural networks, you know, card for decision trees, support vector machines, boosting unayment, as well as classic hypothesis testing and the like and statistics. None of those algorithms are definitely private in their original form. All of them have modifications that add noise to the computation in different places in different ways that achieve differential privacy. So this really means that to the extent that we've become a scientific community very dependent on the use of machine learning and statistical modeling and data analysis, we really do have a path to provide privacy guarantees to those methods and so we can still enjoy the benefits of the data science era while providing rather robust privacy guarantees to individuals.

Hmm, not a real foodie, so I'm a big fan of spaghetti. Yeah, I'm what do you really don't like? I really don't like cauliflower.

No, it's not a stretch. I've not seen those ideas, you know, that is not a technique that to my knowledge will provide differential privacy. But to give an example, like one very specific example about what you're discussing is there was a very interesting project at NYU, I think led by Helen Nissenbaum there, in which they basically built a browser plugin that tried to essentially obfuscate your Google searches. So to the extent that you're worried that Google is using your searches to build predictive models about you to decide what ads to show you, which they might very reasonably want to do. But if you object to that, they built this widget you could plug in. And basically, whenever you put an aquarium to Google, it would send that query to Google, but in the background, all of the time, from your browser, it would just be sending this torrent of irrelevant queries to search engine. So it's like a weed and chaff thing. So out of every thousand queries, let's say that Google was receiving from your browser, one of them was one that you put in, but the other 999 were not. So it's the same kind of idea, kind of privacy by obfuscation. So I think that's an interesting idea. It doesn't give you differential privacy. It's also I was actually talking to somebody at one of the large tech companies recently about the fact that, you know, just this kind of thing that there are sometimes when the response to my data needs to be very specific to my data, right? Like I type mountain biking into Google. I want results on mountain biking and I really want Google to know that I typed in mountain biking. I don't want noise at it so that. And so I think there's sort of maybe even interesting technical questions around notions of privacy that are appropriate where it's not that my date is part of some aggregate, like medical records, and that we're trying to discover important correlations and facts about the world at large. But rather, there's a service that I really want to pay attention to my specific date, and yet I still want some kind of privacy guarantee. And I think these kind of obfuscation ideas are sort of one way of getting at that, but maybe there others as well.

So I'm optimistic about the possibility of balancing the desire for individual privacy and individual control of privacy. with kind of societally and commercially beneficial uses of data, not unrelated to differential privacy or suggestions that say, well, individuals should have control of their data. They should be able to limit the uses of that data. They should even, you know, there's, you know, fledgling discussions going on in research circles about allowing people selective use of their data and being compensated for it. And then you get to sort of very interesting economic questions like pricing, right? And one interesting idea is that maybe differential privacy would also be a conceptual framework in which you could talk about the relative value of different people's data, like, you know, to demystify this a little bit. If I'm trying to build a predictive model for some rare disease, and I'm trying to use machine learning to do it, It's easy to get negative examples because the disease is rare, right? But I really want to have lots of people with the disease in my data set, okay? But so somehow those people's data with respect to this application is much more valuable to me than just like the background population. And so maybe they should be compensated more for it. And so, you know, I think these are kind of very, very fledgling conceptual questions that maybe will have kind of technical thought on them sometime in the coming years. But I do think, well, you know, to kind of get more direct answer to your question. I think I'm optimistic at this point from what I've seen that we will land at some, you know, better compromise than we're at right now, where, again, you know, privacy guarantees are few far between and weak. and users have very, very little control and I'm optimistic that we'll land in something that provides better privacy overall and more individual control of data and privacy. I think to get there, it's again just like fairness, it's not going to be enough to propose algorithmic solutions. There's going to have to be a whole kind of regulatory legal process that prods companies and other parties to kind of adopt solutions.

Yeah, the other thing that has to be said is that it's a cliche, but the users of many systems platforms and apps, we are the product. We are not the customer. The customer are advertisers, and our data is the product. So it's one thing to kind of suggest more individual control of data and privacy and uses. But this If this happens in sufficient degree, it will upend the entire economic model that has supported the internet to date. And so some other economic model will have to be, you know, will have to replace it.

They can be participants in it. And you know, this isn't, you know, this is not a weird idea, right? Because there are markets for data already. It's just that consumers are not participants in one. There's like, you know, there's sort of, you know, publishers and content providers on one side that have inventory and then they're advertised. So another is ended, you know, Google and Facebook are running. They're pretty much their entire revenue stream is by running two-sided markets between those parties. And so it's not a crazy idea that there would be like a three-sided market or that on one side of the market or the other, we would have proxies representing our interest. It's not a crazy idea, but it's not a crazy technical idea, but it would have pretty extreme economic consequences.

Yeah, game theory, of course. Let us get credit where it's due. It comes from the economists first and foremost, but As I've mentioned before, computer scientists never hesitate to wander into other people's turf. And so there is now this 20-year-old field called algorithmic game theory. But game theory, first and foremost, is a mathematical framework for reasoning about collective outcomes in systems of interacting individuals. So you need at least two people to get started in game theory and many people are probably familiar with prisoners dilemma as kind of a classic example of game theory and a classic example where everybody looking out for their own individual interests leads to a collective outcome that's kind of worse for everybody than what might be possible if they cooperate it for example. But cooperation is not an equilibrium in prisoners dilemma. And so my work and the field of algorithmic game theory more generally in these areas kind of looks at settings in which the number of actors is potentially extraordinarily large and their incentives might be quite complicated and kind of hard to model directly, but you still want kind of algorithmic ways of kind of predicting what will happen or influencing what will happen in the design of platforms.

There's a lot of them. I'm a big fan of the field. I mean, technical answers to that, of course, would include nashes work, just establishing that there is a competitive equilibrium under very, very general circumstances, which in many ways put the field on a firm conceptual footing, because if you don't have equilibrium, it's hard to ever reason about what might happen since there's no stability.

I mean, I'm not that it will necessarily emerge just that it's possible, right? Like the existence of equilibrium doesn't mean that sort of natural iterative behavior will necessarily lead to it.

Yeah. Maybe answering a slightly less personally than you asked the question. I think within the field of algorithmic game theory, perhaps the single most important kind of technical contribution that's been made is the realization between close connections between machine learning and game theory and in particular between game theory and the branch of machine learning that's known as no regret learning. And this sort of provides a very general framework in which a bunch of players interacting in a game or a system. Each one kind of doing something that's in their self-interest will actually kind of reach an equilibrium and actually reach an equilibrium in a pretty, you know, a rather, you know, short amount of steps.

Yeah. Right. And stability or equilibrium by itself is not necessarily either a good thing or a bad thing.

Well, I mean, we kind of talked about these ideas already in kind of a non-technical way, which is maybe the more interesting way of understanding them first, which is, you know, we have many systems, platforms, and apps these days that work really hard to use our data and the data of everybody else on the platform to selfishly optimize on behalf of each user. So let me give the cleanest example, which is just driving apps, navigation apps like Google Maps and Ways, where miraculously compared to when I was growing up at least, You know, the objective would be the same when you wanted to drive from point A to point B, spend the least time driving. Not necessarily minimize the distance, but minimize the time, right? And when I was growing up, like the only resources you had to do that were like maps in the car, which literally just told you what roads were available. And then you might have like half-hour lead traffic reports, just about the major freeways, but not about side roads. So you were pretty much on your own. And now, we've got these apps. You pull it out and you say, I want to go from point A to point B, and in response kind of to what everybody else is doing, if you like what all the other players in this game are doing right now, here's the root that minimizes your driving time. So it is really kind of computing a selfish, best response for each of us in response to what all of the rest of us are doing at any given moment. And so, you know, I think it's quite fair to think of these apps as driving or nudging us all towards the competitive or Nash equilibrium of that game. Now, you might ask like, well, that sounds great. Why is that a bad thing? Well, you know, it's known both in theory and with some limited studies from actual, like, traffic data that All of us being in this competitive equilibrium might cause our collective driving time to be higher, maybe significantly higher, then it would be under other solutions. And then you have to talk about what those other solutions might be and what the algorithms to implement, though, are, which we do discuss in the kind of game theory chapter of the book. But similarly, on social media platforms, or on Amazon, all these algorithms that are essentially trying to optimize our behalf, they're driving us in a colloquial sense towards some kind of competitive equilibrium. And one of the most important lessons of game theory is that just because we're at equilibrium doesn't mean that there's not a solution in which some or maybe even all of us might be better off. And then the connection to machine learning, of course, is that, you know, all these platforms I've mentioned, the optimization that they're doing on our behalf is driven by machine learning, you know, like predicting where the traffic will be predicting what products I'm going to like predicting what would make me happy in my news feed.

just to prime your viewers a little bit. I mean, I think you're referring to the fact that Game Theory was taken quite seriously back in the 60s as a tool for reasoning about kind of Soviet U.S. nuclear armament, disarmative, detente, things like that. I'll be honest, as huge of a fan as I am a game theory and it's kind of rich history. It still surprises me that you had people at the ran corporation back in those days kind of drawing up, you know, two by two tables and one, the row player is, you know, the U.S. and the column player is Russia and that they were taking seriously, you know, I'm sure if I was there, maybe it wouldn't have seemed as naive as it does at the time.

Yeah, even though I kind of laugh at those efforts, they were more sensible then than they would be now, right? Because there were sort of only two nuclear powers at the time, and you didn't have to worry about deterring new entrants and who was developing the capacity. And so we have many, you know, we have, it's definitely a game with more players now and more potential entrants. I'm not in general somebody who advocates using kind of simple mathematical models when the stakes are as high as things like that and the complexities are very political and social, but we are still here.

Yeah, it's a good question. I mean, in the time I've spent on Wall Street and in finance, you know, I've seen a clear progression and I think it's a progression that kind of models the use of algorithms and automation more generally in society, which is, you know, the things that kind of get taken over by the algos first are sort of the things that computers are obviously better at than people, right? So, You know, so first of all, there needed to be this era of automation, right, where just, you know, financial exchanges became largely electronic, which then enabled the possibility of, you know, trading becoming more algorithmic, because once, you know, that exchanges are electronic, and algorithm can submit in order through an API, just as well as a human can do at a moment or quickly, can read all the data.

And so I think the places where algorithmic trading have had the greatest inroads and had the first inroads were in kind of execution problems, kind of optimized execution problem. So what I mean by that is at a large brokerage firm, for example, one of the lines of business might be on behalf of large institutional clients taking, you know, what we might consider difficult trade. So it's not like a mom and pop investor saying, I want to buy 100 shares of Microsoft. It's a large hedge fund saying, you know, I want to buy a very, very large stake in Apple. And I want to do it over the span of a day. And it's such a large volume that if you're not clever about how you break that trade up, not just over time, but over perhaps multiple different electronic exchanges that all let you trade Apple on their platform. You will move, you'll push prices around in a way that hurts your execution. This is an optimization problem. This is a control problem. machines are better. We know how to design algorithms that are better at that kind of thing. Then a person is going to be able to do because we can take volumes of historical and real-time data to optimize the schedule with which we trade. And similarly, high frequency trading, which is closely related, but not the same as optimized execution. where you're just trying to spot very, very temporary, you know, misprice things between exchanges or within an asset itself or just predict directional movement of a stock because of the kind of very, very low level granular by and selling data in the exchange. Machines are good at this kind of stuff.

Yeah. So I think we are in an era where clearly there have been some very successful, you know, quant hedge funds that are, you know, in what we would traditionally call, you know, still in the stat-arb regime.

Stadard referring to statistical arbitrage, but for the purposes of this conversation, what it really means is making directional predictions in asset price movement or returns. Your prediction about that directional movement is good for, you know, you have a view that it's valid for some period of time between a few seconds and a few days. And that's the amount of time that you're going to kind of get into the position, hold it, and then hopefully be right about the directional movement, and, you know, by-low and sell high as the cliche goes. So, that is a, you know, kind of a sweet spot, I think, for quant trading and investing right now and has been for some time. When you really get to kind of more Warren Buffett style time scales, right? Like, you know, my cartoon of Warren Buffett is that Warren Buffett sits and thinks what the long-term value of Apple really should be. And he doesn't even look at what Apple is doing today. He just decides, you know, I think that this is what its long-term value is. And it's far from that right now. And so I'm going to buy some Apple or short some Apple. And I'm going to sit on that for 10 or 20 years. So when you're at that kind of time scale or even more than just a few days, all kinds of other sources of risk and information. You know, so now you're talking about holding things through recessions and economic cycles. Wars can break out. So there you have to follow a team of nature at a level. Yeah, and you need to just be able to ingest many, many more sources of data that are on wildly different timescales, right? So if I'm an HFT, my high frequency trader, like I don't, I don't I really, my main source of data is just the data from the exchanges themselves about the activity in the exchanges, right? And maybe I need to pay, you know, I need to keep an eye on the news, right? Because, you know, that can suddenly cause sudden, you know, the, you know, CEO gets caught in a scandal or, you know, gets run over by a bus or something that can cause very sudden changes. But I don't need to understand economic cycles. I don't need to understand recessions. I don't need to worry about the political situation or war breaking out in this part of the world because all I need to know is as long as that's not going to happen in the next 500 milliseconds, then my model is good. When you get to these longer time scales, you really have to worry about that kind of stuff. People in the machine learning community are starting to think about this. We jointly sponsored a workshop at 10 with the Federal Reserve Bank of Philadelphia a little more than a year ago. I think the title is something like machine learning for macroeconomic prediction. macroeconomic referring specifically to these longer time scales. It was an interesting conference, but it left me with greater confidence that we have a long way to go to, you know, and so I think that people that, you know, in the grand scheme of things, you know, if somebody asks me like, well, who's job on Wall Street is safe from the bots? I think people that are at that longer, you know, time scale and have that appetite for all the risks involved in long-term investing. and that really need kind of not just algorithms that can optimize from data, but they need views on stuff. They need views on the political landscape, economic cycles, and the like. And I think, you know, they're pretty safe for a while, as far as I can tell. So Warren Buff is job is not seeing, you know, a Robo Warren Buffett any time. So give him comfort.

I'll answer slightly different questions, which is like, what's this a day in my life or my career that was kind of a watershed moment? I went straight from undergrad to doctoral studies, and that's not at all a typical. And I'm also from an academic family. My dad was a professor, my uncle, on his side as a professor, both my grandfather's work professors.

Yeah. And I was a grad student here, just up the river at Harvard, and came to study with less valiant, which was a wonderful experience. But I remember my first year of graduate school, I was generally pretty unhappy. And I was unhappy because at Berkeley as an undergraduate, you know, yeah, I studied a lot of math and computer science. But it was a huge school first of all. And I took a lot of other courses as we discussed. I started as an English major and took history courses and art history classes and had friends that did all kinds of different things. And Harvard's a much smaller institution than Berkeley. And it's computer science department, especially at that time, was a much smaller place than it is now. And I suddenly just felt very, I'd gone from this very big world to this highly specialized world. And now all of the classes I was taking were computer science classes and I was only in classes with math and computer science people. And so I was, you know, I thought often in that first year of grad school about whether I really wanted to stick with it or not. And, you know, I thought, like, oh, I could, you know, stop with a master's, I could go back to the Bay Area and to California. And, you know, this was some one of the early periods where there was, you know, like, you could definitely get a relatively good job paying job at one of the tech companies back, you know, that were the big tech companies back then. And so I distinctly remember like kind of a late spring day when I was kind of, you know, sitting in Boston, calm and kind of really just kind of chewing over what I wanted to do with my life. And I realized, okay, and I think this is where my academic background helped me a great deal. I sort of realized, you know, yeah, you're not having a great time right now. This feels really narrowing, but you know that you're here for research eventually and to do something original and to try to, you know, carve out a career where you kind of, you know, choose what you want to think about, you know, and have a great deal of independence. And so, you know, at that point, I really didn't have any real research experience yet. I mean, it was trying to think about some problems with very little success. But I knew that like I hadn't really tried to do the thing that I knew I'd come to do. And so I thought, I'm going to stick through it for the summer, and that was very formative, because I went from kind of contemplating quitting to a year later, it being very clear to me I was going to finish, because I still had a ways to go, but I kind of started doing research. It was going well. It was really interesting, and it was sort of a complete transformation. You know, this is that transition that I think every doctoral student makes at some point which is to sort of go from being like a student. of what's been done before to doing, you know, your own thing and figuring out what makes you interested in what your strengths and weaknesses are as a researcher. And once, you know, I kind of made that decision on that particular day at that particular moment in Boston Common, you know, I'm glad I made that decision.

Thank you so much for coming back and touching with you too and see how great you're doing as well. Thank you. Thanks a lot. Appreciate it.