The following is a conversation with Michael Kearns. He's a professor at the University of
Pennsylvania and a co-author of the new book, Ethical Algorithm, that is the focus of much of
this conversation. It includes algorithmic fairness, bias, privacy, and ethics in general.
But that is just one of many fields that Michael is a world-class researcher in,
some of which we touch on quickly, including learning theory or the theoretical foundation
of machine learning, game theory, quantitative finance, computational social science, and much
more. But on a personal note, when I was an undergrad, early on, I worked with Michael on
an algorithmic trading project and competition that he led. That's when I first fell in love
with algorithmic game theory. While most of my research life has been in machine learning and
human-robot interaction, the systematic way that game theory reveals the beautiful structure in
our competitive and cooperating world of humans has been a continued inspiration to me. So for that
and other things, I'm deeply thankful to Michael and really enjoyed having this conversation again
in person after so many years. This is the Artificial Intelligence Podcast. If you enjoy it,
subscribe on YouTube, give it five stars on Apple Podcasts, support on Patreon,
or simply connect with me on Twitter. Alex Friedman spelled F-R-I-D-M-A-N. This episode
is supported by an amazing podcast called Pessimists Archive. Jason, the host of the show,
reached out to me looking to support this podcast, and so I listened to it to check it out. And by
listened, I mean I went through it, Netflix binge style, at least five episodes in a row.
It's not one of my favorite podcasts, and I think it should be one of the top podcasts in the world,
frankly. It's a history show about why people resist new things. Each episode looks at a moment
in history when something new was introduced, something that today we think of as commonplace,
like recorded music, umbrellas, bicycles, cars, chess, coffee, the elevator, and the show explores
why it freaked everyone out. The latest episode on mirrors and vanity still stays with me as I think
about vanity in the modern day of the Twitter world. That's the fascinating thing about the show,
is that stuff that happened long ago, especially in terms of our fear of new things,
repeats itself in the modern day, and so has many lessons for us to think about
in terms of human psychology and the role of technology in our society.
Anyway, you should subscribe and listen to Pessimist Archive. I highly recommend it.
And now, here's my conversation with Michael Kearns. You mentioned reading
Fear and Loading Las Vegas in high school, and having more or a bit more of a literary mind.
So, would books, non-technical, non-computer science,
would you say had the biggest impact on your life, either intellectually or emotionally?
You've dug deep into my history, I see.
Going deep. Yeah, I think my favorite novel is Infinite Jest 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, when I was in high
school, I actually even started college as an English major. So, I was very influenced by sort
of that genre of journalism at the time and thought I wanted to be a writer and then realized 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. Well, in your new book, Ethical Algorithm,
you kind of sneak up from an algorithmic perspective on these deep, profound philosophical
questions of fairness, of privacy. In thinking about these topics, how often do you return to
that literary mind that you had? 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, I kind of consider myself primarily and 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 buzzkill 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. 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 run ideas behind him. So I mean, I remember an early email I sent to him
in which I said like, oh, here's a specific definition of algorithmic fairness that we think
is some sort of variant of Rawlsian fairness. What do you think? And I thought I was asking a yes
or no question. And I got back here, kind of classical philosophers are responding, 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, you know, 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 the algorithmic solutions take care of some of the
low hanging fruit. Sort of the problem is a lot of algorithms, when they don't consider fairness,
they are just terribly unfair. And when they don't consider privacy, they're terribly,
they violate privacy. Sort of the algorithmic approach fixes big problems. But there's still,
you get, when you start pushing into the gray area, that's when you start getting to this
philosophy of what it means to be fair, starting from Plato, what is justice kind of questions?
Yeah, I think that's right. And I mean, I would even not go as far as you went to say that sort
of the algorithmic work in these areas is solving like the biggest problems. And, you know, 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 literature already, you
can see it's going to be much more of a mess. And you know, you've got these theorems saying,
here are three entirely reasonable, desirable notions of fairness. And, you know, 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, you know, many generations of scholars before us. So it's very early days for fairness, I think.
So before we get into the details of differential privacy and then the fairness side,
I mean, linger on the philosophy, but do you think most people are fundamentally good?
Or do most of us have both the capacity for good and evil within us?
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 due to people
being bad at heart. With people with power are people at the heads of governments,
people at the heads of companies, people at the heads of maybe so financial power markets.
Do you think the distribution there is also most people are good and have good intent?
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, you know, the cliche about absolute power corrupts absolutely.
I mean, you know, I think even short of that, you know, 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 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 your maybe slippery slope
is too strong of a word, but, you know, 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, there being good people and
evil people. And it's somehow the evil people are the ones that somehow rise to power.
That's really interesting. So it's the within the social norms constructed by that particular
group of people, you're all trying to do good. But because it's a group, you might be, you might
drift into something that for the broader population, it does not align with the values of
society. That kind of, that's the word. Yeah. I mean, or not that you drift, but even that
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, you know, like, so for instance, you know, on, 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, you're, you're, and you learn more, you, your view does become more
nuanced, right? You realize, okay, there is actually a function to this activity. And for, in 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 that'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 all of you people doing? Why are you paid so much?
In some cases, a taxpayer expenses to do, you know, to publish papers that nobody 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.
Right. And in the case of the financial sector, tools like credit might not make sense to people.
Like it's a good example of something that does seem to pop up and be useful or just the power
of markets and just in general capitalism. Yeah. In 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, right? 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, you know, if the fact
of the matter is that, you know, sort of on some particular time scale, you are holding positions
that are, you know, very unlikely to, you know, lose, you know, they're like your value at risk
or variance is like one or 5%, then it kind of makes sense that you would be allowed to use a
little bit more than you have, because you have, you know, 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, you
know, not too long ago. But, you know, but the idea that it serves no useful economic purpose
under any circumstances is definitely not true. We'll return to the other side of the coast,
Silicon Valley, and the problems there as we talk about, privacy as we talk about fairness.
At the high level, and I'll ask some sort of basic questions with the hope to get at the
fundamental nature of reality, but from a very high level, what is an ethical algorithm? So,
I can say that an algorithm has a running time of using big O notation and log n. I can say that
a machine learning algorithm classifies cat versus dog with 97% accuracy. Do you think there will one
day be a way to measure sort of in the same compelling way as the big O notation of this
algorithm is 97% ethical? First of all, let me riff for a second on your specific n log n examples.
So, because early in the book, when we're just kind of trying to describe algorithms, period,
we say like, okay, you know, what's an example of an algorithm or an algorithmic 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,
you know, it's quadratic rather than n log n, which we know is kind of optimal for sorting.
And we make the point that sort of like, you know, so even within the confines of a very
precisely specified problem, there, you know, there might be many, many different algorithms
for the same problem with different properties, like some might be faster in terms of running
time, some might use less memory, some might have, you know, better distributed implementations.
And, and so the point is, is that already we're used to, you know, in computer science, thinking
about tradeoffs between different types of quantities and resources, and there being, you
know, better and worse algorithms. And, and our book is about that part of algorithmic ethics
that we know how to kind of put on that same kind of quantitative footing right now.
So, you know, 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, right? But if you pick one of them, you can
do something akin to saying that this algorithm is 97% ethical. You can say, for instance, the,
you know, for this lending model, the false rejection rate on black people and white people
is within 3%, right? So we might call that to a 97% ethical algorithm and a 100%
ethical algorithm would mean that that difference is 0%.
In that case, fairness is specified when two groups, however they're defined, are given to you.
That's right.
So the, and then you can sort of mathematically start describing the algorithm, but
nevertheless, the part where the two groups are given to you, I mean, unlike running time,
you know, we don't, in computer science, talk about how fast an algorithm feels like when it
runs. True. We measure it and ethical starts getting into feelings. So for example, an algorithm
runs, you know, if it runs in the background, it doesn't disturb the performance of my system.
It'll feel nice. I'll be okay with it. But if it overloads the system, it'll feel unpleasant.
So in that same way, ethics, there's a feeling of how socially acceptable it is. How does it
represent the, the moral standards of our society today? So in that sense, and sorry to link around
that, first of all, high level philosophical question is, do you have a sense we'll be able
to measure how ethical an algorithm is? First of all, I didn't, certainly didn't mean to give
the impression that you can kind of measure, you know, memory speed tradeoffs, you know, and, and
that there's a complete, you know, mapping from that onto kind of fairness, for instance, or ethics
and, 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 it 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 a 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 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 will 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 a loan, incorrectly denied a loan, all of these definitions basically say like, well,
you know, your compensation is the knowledge that we are, we are also falsely denying loans to
other people, you know, in other groups at the same rate that we're doing it to you. And, and,
you know, there 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, right? They, they might really feel
like their particular interests are being protected or thought about by the algorithm rather than
just, you know, the groups that they happen to be members of.
Is there parallels to the big O notation of worst case analysis? So, is it important to
looking at the worst violation of fairness for an individual? Is it important to minimize that one
individual? So like worst case analysis? Is that something you think about or?
I mean, I think we're not even at the point where we can sensibly think about that. So,
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, 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 call 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 things 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. There's, you know, we just started doing a little bit of work in our group at
actually doing kind of human subject experiments in which we, you know, ask people about, you know,
about, you know, we ask them questions about fairness. We survey them. We, you know, we show
them pairs of individuals in, 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? And
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 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, you know, there's very little literature on, you know, sort of 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, which is so one of my deep passions is psychology. And I always thought computer scientists
will be the best future psychologists in the sense that data is especially in this modern world,
the data is a really powerful way to understand and study human behavior. And you've explored
that with your game theory side of work as well. 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 just computer science, but AI and machine
learning more specifically, I kind of think of as imperialist research communities in that,
you know, 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 theorists that looked at this and said, like,
okay, you are reinventing stuff that we've known since like the 40s, right? But, you know, in my
view, eventually, this sort of, you know, computer scientists have made significant contributions to
that field, even though we kind of embarrassed ourselves for the first decade. So I think of
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, you know, some other areas that we've waited into.
So you kind of mentioned this, just to linger on the idea of an ethical algorithm,
of idea of groups, sort of group thinking and individual thinking, and we're struggling that,
one of the amazing things about algorithms and your book and just this field of study is
is it gets us to ask, like, forcing machines, converting these ideas into algorithms is forcing
us to ask questions of ourselves as a human civilization. So there's a lot of people now
in public discourse doing sort of group thinking, thinking like there's particular
sets of groups that we don't want to discriminate against and so on. And then there is individuals,
sort of in the individual life stories, the struggles they went through and so on. Now,
like, in philosophy, it's easier to do group thinking because you don't, you know, it's very
hard to think about individuals that there's so much variability. But with data, you can start
to actually say, you know, what group thinking is too crude, you're actually doing more discrimination
by thinking in terms of groups and individuals. Can you linger on that kind of idea of group
versus individual and ethics? And is it good to continue thinking in terms of groups and algorithms?
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, you know, for none of these groups, do we, you know, have a false negative rate,
which is much higher than any other one of these groups. Okay. So these are kind of classic group
aggregate notions of fairness. And, you know, but at the end of the day, an individual you can think
of as a combination of all their attributes, right? They're a member of a racial group, they're,
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, are still, you know, very strong determinants of
outcome and personality and the like. 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, so in particular, you know,
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, 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 the optimization, actually, that's a fascinating way to put it.
So you're just optimizing, it's the one way to achieve the optimizing fairness for individuals,
just to add more and more definitions of groups that each individual belongs to.
That's right. So, you know, 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. Okay. And so it is possible to put, you know, these incredibly
coarse ways of thinking about fairness and these very, very individualistic specific ways on a
common scale. And, you know, one of the things we've worked on from a research perspective is,
you know, so we sort of know how to, you know, we, in relative terms, we know how to provide
fairness guarantees at the course ascend 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, you know, refined subgroups. I mean, we gave a name to this
phenomenon where, you know, you protect, you enforce some 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 know,
you're giving some guarantee at the aggregate level. Yes. But when you kind of look in a more
granular way at what's going on, you realize that 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 end of the spectrum. Does there need to be human input in the form of
weighing the value of the importance of each kind of group? So for example, is it,
is it like, so gender, say, crudely speaking, male and female, and then different races,
are we as humans supposed to put value on saying gender is 0.6 and race is 0.4 in terms of in the
big optimization of achieving fairness? Is that kind of what humans are supposed to do here?
I mean, of course, you know, I don't need to tell you that, of course, technically,
one could incorporate such weights if you wanted to into a definition of fairness.
You know, fairness is an 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, I mean, 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, and, you know,
you quickly find yourselves kind of revisiting topics that have been kind of unresolved forever,
like affirmative action, right? Sort of, 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, of retribution. Other people will say it's a matter
of economic opportunity. And 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 that 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, right? You know, where people talk about fairness
in criminal sentencing or, you know, predicting failures to appear or making parole decisions
or the like, they will, you know, they'll point out that, well, these definitions of fairness are
all about fairness for the criminals. And what about fairness for the victims, right? So when I
basically say something like, well, the, the false incarceration rate for black people and white
people needs to be roughly the same. You know, there's no mention of potential victims of criminals
in such a fairness definition. And that's the realm of public discourse. I should actually
recommend, I just listened to, to people listening, Intelligent Squares Debates, U.S.
edition just had a debate, they have this structure where you have an old Oxford style
or whatever they're called debates, those two versus two, and they talked about affirmative
action. And it was incredibly interesting that it's still, there's really good points on every
side of this issue, which is fascinating to listen to. Yeah, yeah, I agree. And so it's,
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 and expect
people to take it seriously if you're unwilling to engage in these broader debates that are,
are entirely extra algorithmic, right? They're, they're, they're not about,
you know, algorithms and making algorithms better. They're sort of, you know, as you said,
sort of like, what should society be protecting in the first place?
When you discuss a fairness, an algorithm that, that achieves fairness, whether in the constraints
and the objective function, there's an immediate kind of analysis you can perform, which is saying,
if you care about fairness in gender, this is the amount that you have to pay for it in terms of
the performance of the system. Like, do you, is there a role for statements like that in a table
and a paper, or do you want to really not touch that? Like, no, we want to touch that and we do
touch it. So I mean, just, just again, to make sure I'm not promising your, your viewers more than
we know how to provide. But if you pick a definition of fairness, like I'm worried about
gender discrimination, and you pick a notion of harm, like false rejection for a loan, for example,
and you give me a model, I can definitely first of all go audit that model. It's easy for me to go,
you know, from data to kind of say like, okay, your false rejection rate on women is this much
higher than it is on men. Okay. But, you know, once you also put the fairness into your objective
function, I mean, I think the table that you're talking about is, you know, 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, you know, 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 unfair, you know, we, you know, if the two axes are air and unfairness,
we'd like to be at zero zero, we'd like zero error and zero fair 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 that that's not going to happen. But in
general, you know, you'll get this, you'll get some kind of convex curve that specifies the numerical
tradeoff you face, you know, if I want to go from 17% error down to 16% error, what will be the
increase in unfairness that I experienced as a result of that. And, 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, you know,
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, you know, that's accuracy. But if you also care about
those, you know, the mistakes you make not being disproportionately on one racial group or another,
you can, 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 have, that are the ones that have to make the decision,
where do we want to be on this curve? Like, 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 trade-offs generally exist and that you need to pick a point and,
like, 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.
Just to link down the point of trade-offs, I think that's a really important thing to sort of
think about. So, you think when we start to optimize for fairness, there's almost always,
in most systems, going to be trade-offs. So, can you, like, what's the trade-off between?
Just to clarify, there have been some sort of technical terms thrown around, but a sort of
a sort of a perfectly fair world. Why is that, why will somebody be upset about that?
The specific trade-off I talked about just in order to make things very concrete was between
numerical error and some numerical measure of unfairness.
What is numerical error in the case of...
Just, like, say predictive error, like, you know, the probability or frequency with which you
to 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.
So, in the case of awarding somebody parole or giving somebody parole or letting them out
on parole, you don't want them to recommit a crime. So, it's your system failed in prediction
if they happen to do a crime. Okay. So, that's the perform... That's one axis.
Right. And what's the fairness axis?
So, then the fairness axis might be the difference between racial groups in the kind of false
positive predictions, namely people that I kept incarcerated,
predicting that they would recommit a violent crime when, in fact, they wouldn't have.
Right. And the unfairness of that, just to linger it, and allow me to
ineliquently to try to sort of describe why that's unfair, why unfairness is there.
The unfairness you want to get rid of is that in the judge's mind, the bias of having being
brought up to society, the slight racial bias, the racism that exists in the society, you want to
remove that from the system. Another way that's been debated is sort of equality of opportunity
versus equality of outcome. And there's a weird dance there that's really difficult to get right.
And we don't... It's what the affirmative action is exploring that space.
Right. And then we... This also quickly 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. Right. And so there's always in kind of fairness discussions, the
possibility that the real injustice came earlier, right? Earlier in this individual's life, earlier
in this group's history, et cetera, et cetera. And so a lot of the fairness discussion is almost...
The goal is for it to be a corrective mechanism to account for the injustice earlier in life.
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 not incarcerate, falsely incarcerate more people of one group than another group. But if
you... But I mean, do you think just... 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 era, right? And I think many of your viewers have probably heard these examples before.
But let's say I'm building a face recognition system, right? And so I'm kind of gathering lots
of images of faces and trying to train the system to recognize new faces of those individuals from
training on a training set of those faces of individuals. And 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 the model to maximize the overall accuracy on
my training dataset that the model can reduce its error most by getting things right on the white
males that constitute the majority of the dataset, even if that means that on other groups,
they will be less accurate. 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 optimize the error at the
expense of this discrimination. And then you're kind of back in the land of these kind of two-dimensional
numerical trade-offs. A valid counterargument is to say like, well, no, you don't have to...
There's no... The notion of the tension between error and accuracy here 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 equalize your dataset. Or you could train a separate model on those subgroups and have
multiple models. The point I think we tried to make in the book is that those things have cost
too. Going out and gathering more data on groups that are relatively rare compared to your plurality
or your majority group, it may not cost you in the accuracy of the model, but 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 in training a model, you might push the cost
somewhere else like money, like development time, research time, and the like.
There are fundamentally difficult philosophical questions in fairness. And we live in a very
divisive political climate, outraged culture. There is alt-right folks on 4chan trolls. There is
social justice warriors on Twitter. There is very divisive, outraged folks on all sides of every
kind of system. How do you, how do we as engineers build ethical algorithms in such divisive culture?
Do you think they could be disjoint? The human has to inject your values, and then you can
optimize over those values. But in our times, when you start actually applying these systems,
things get a little bit challenging for the public discourse. How do you think we can proceed?
Yeah, I mean, for the most part in the book, 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 is just one step away from that. But I do think, obviously, despite disclaimer that people
like us shouldn't be making those decisions for society, we are 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. But they had to make, so if we look at people at the
heads of companies and so on, they had to make those decisions. There has to be decisions,
so there's two options. Either you kind of put your head in the sand and don't think about these
things and just let the algorithm do what it does, or you make decisions about what you value,
you know, injecting moral values into the algorithm. Look, I don't, I never mean to be an
apologist for the tech industry, but I think 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 in technology and computer science, a lot of these platforms that
we now use regularly kind of started as curiosities, right? I remember when things like Facebook came
out and its predecessors like Friendster, which nobody even remembers now, people really wonder
like why would anybody want to spend time doing that? You know, I mean even the web when it first
came out, when it wasn't populated with much content and it was largely kind of hobbyists
building their own kind of ramshackle 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 you know, I don't think
10 years ago, anyone anticipated that those platforms, for instance, might kind of
acquire undue 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 the 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 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, you know,
these are systems, right? 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. And so, you know,
you know, I'm kind of like, who really knew that, you know, until we saw it happen, who knew that
these things might be able to influence the outcome of elections, who knew that, you know,
they might polarize political discourse because of the ability to, you know, decide who you interact
with on the platform and also with the platform naturally using machine learning to optimize
for your own interests, that they would further isolate us from each other and, you know, like
feed us all basically just the stuff that we already agreed with. And so I think, you know,
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. Now, you asked like, well,
are there algorithmic remedies to these kinds of things? And again, these are big problems
that are not going to be solved with, you know, 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 newsfeed, you know, or other platforms has led
to polarization and intolerance of opposing viewpoints. As you know, right, these, these
algorithms have models, right, and they kind of place people in some kind of metric space and,
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. Okay. So, you know, in this case, there really is some knob you could tune that 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 is at least further from your interest. Like, it's almost like an exploration button.
So just get your intuition. Do you think engagement, so like you're staying on the
platform, you're staying engaged. Do you think fairness, ideas of fairness won't emerge? Like,
how bad is it to just optimize for engagement? Do you think we'll run into big trouble if we're
just optimizing for how much you love the platform? Well, I mean, optimizing for engagement kind of
got us where we are. So do you, one, have faith that it's possible to do better? And two, if it is,
how do we do better? 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.
In the short term, at least.
Yeah, in the short term, right. And again, you know, if I worked at these companies, I'm sure that
it would have seemed like the most natural thing in the world also to want to optimize
engagement, right? And that's good for users in some sense. You want them to be, you know,
vested in the platform and enjoying it and finding it useful, interesting and or productive.
But, you know, my point is, is that the idea that there is, 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, right? I mean, these companies are driven by
machine learning and this optimization of engagement is essentially driven by machine
learning, right? It's driven by not just machine learning, but, you know, very, very large scale
A, B experimentation where you kind of 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, is that any time you know how to optimize for something, you know,
by def, almost by definition, that solution tells you how not to optimize for it or to do
something different. Engagement can be measured. So, sort of optimizing for sort of minimizing
divisiveness or maximizing intellectual growth over the lifetime of a human being
are very difficult to measure. That's right. So, I'm not, I'm not claiming that
doing something different will immediately make it apparent that this is a good thing
for society. And 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 myopically 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 newsfeed 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 myopically optimize 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. And if Mark Zuckerberg gave you a call and said, I'm thinking of taking
a sabbatical, could you run Facebook for me for six months? What would you how?
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. Yes. And so I don't really think I would need to be CEO of Facebook to kind of implement
the, you know, more limited set of solutions that I might imagine. But I think one concrete thing
they could do is they could experiment with letting people who chose to, to see more stuff in their
newsfeed that is not entirely kind of chosen to optimize for their particular interests, beliefs,
etc. So the kind of thing is like I speak to YouTube, but I think Facebook probably does
something similar is they're quite effective at automatically finding what sorts of groups you
belong to, not based on race or gender or so on, but based on the kind of stuff you enjoy watching
in the case of YouTube. Sort of it's a difficult thing for Facebook or YouTube to then say,
well, you know what, we're going to show you something from a very different cluster.
Even though we believe algorithmically, you're unlikely to enjoy that thing.
Sort of that's a weird jump to make. There has to be a human like at the very top of
that system that says, well, that will be long term healthy for you. That's more than an algorithmic
decision. Or that same person could say that'll be long term healthy for the platform or 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 they shouldn't be completely
alien ideas. You could try things like this and it wouldn't be, we wouldn't have to invent
entirely new science to do it because if we're all already embedded in some metric space and
there's a notion of distance between you and me and every piece of content, then we know the same
model that dictates how to make me really happy also tells how to make me as unhappy as possible
as well. The focus in your book and algorithmic fairness research today in general is on machine
learning, like we said, is data. Just even the entire AI field right now is captivated with
machine learning, with deep learning. Do you think ideas in symbolic AI or totally other
kinds of approaches are interesting, useful in the space, have some promising ideas in terms of
fairness? 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 notably closer to general artificial intelligence now than we were when I started
my career. There's been progress. I do think that we are as a community maybe looking at
that where the light is, but the light is shining pretty bright there right now and we're finding
a lot of stuff. I don't want to argue with the progress that's been made in areas like deep
learning, for example. This touches on another related thing that you've mentioned and that
people might misinterpret from the title of your book, Ethical Algorithm. Is it possible for the
algorithm to automate some of those decisions, higher level decisions of what should be fair?
The more you know about a field, the more aware you are of its limitations. I'm pretty leery
of trying. There's so much 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. The idea of letting the algorithm start exploring as well, I definitely think
this is a much narrower statement. I definitely think that algorithmic auditing for different
types of unfairness. 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 quickly get to a point where there's a lot of categories. There's a lot of combinations of
end features. You can use algorithmic techniques to try to find the subgroups on which you're
discriminating the most and try to fix that. That's actually the form of one of the algorithms we
developed for this fairness gerrymandering problem. Partly because 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. 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, not only have we figured fairness out, but we think the algorithms should start
deciding what's fair or giving input on that decision. The cost-benefit analysis to the field
of going there right now just doesn't seem worth it to me.
That said, I should say that I think computer scientists should enrich their thinking about
these kinds of things. I think it's been too often used as an excuse for roboticists working on
autonomous vehicles, for example, to not think about the human factor or psychology or safety
in the same way like computer science design algorithms that have been sort of using as an
excuse. I think it's time for basically everybody to become computer scientists.
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 wade out into the world more. I mean, just the influence that computer science
and therefore computer scientists have had on society at large has exponentially magnified
in the last 10 or 20 years or so. Before, when we were just tinkering around amongst ourselves
and it didn't matter that much, there was no need for computer scientists to be citizens of the
world more broadly. I think those days need to be over very, very fast. I'm not saying everybody
needs to do it, but to me, the right way of doing it is to not 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. I think one of the reasons we decided to write this book is
we thought 10 years ago, I wouldn't have tried this just because I just didn't think that people's
awareness of algorithms and machine learning, the general population would have been high.
I mean, you would have had to first 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
lots of people without any technical training at all know enough about algorithms and 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, so just to linger on the disagreement, because I do disagree with you on the point that
I think if you're a biologist, if you're a chemist, if you're an MBA business person,
all of those things, if you learn to program, and not only program, if you learn to do machine
learning, if you learn to do data science, you immediately become much more powerful in the
kinds of things you can do. And therefore, literature, library sciences, so you were
speaking, I think, I think it holds true what you're saying for the next few years, but long term,
if you're interested to me, if you're interested in philosophy, you should learn to program because
then you can scrape data and study what people are thinking about on Twitter and then start
making philosophical conclusions about the meaning of life. I just feel like the access to data,
the digitization of whatever problem you're trying to solve, it fundamentally changes what
it means to be a computer scientist. To me, a computer scientist in 20, 30 years will go back
to being a Donald Knuth-style theoretical computer science, and everybody would be doing basically
exploring the kinds of ideas that you're exploring in your book. It won't be a computer science.
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 people will look back and having a computer science department will look like having
an electricity department or something. It's like, everybody uses this, it's just out there.
I mean, I do think there will always be that kind of Knuth-style core to it, 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 infiltrating all of science and society
and the humanities, etc. 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, you know, the literature and practice of things like data
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, you know, something to
go do with data. But, you know, our view is that it's, and I think this is now, you know,
quite widespread that it's, you know, 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, you know, 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 anonymization notions while
still letting us do useful stuff with data. What's anonymization of data?
So, by anonymization, I'm, you know, kind of referring to techniques like I have a database,
the rows of that database are, let's say, individual people's medical records, okay?
And 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, you know,
so that would be like a redaction, right? I'm just redacting that information. I am going to
take ages, and I'm not going to, like, say your exact age, I'm going to say whether you're,
you know, zero to 10, 10 to 20, 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 at a slightly broader picture, as you talk about, what does anonymization mean
when you have multiple database, like with a Netflix prize, when you can start combining
stuff together. So this is exactly the problem with these notions, right? Is that notions of
a done anonymization, removing personally identifiable information, the kind of 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 know, you can re-identify people
that were, you know, 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 for people who don't know, the Netflix prize was what was being publicly released
as data. So the names from those rows were removed. But what was released is the preference or the
ratings of what movies you like and don't like. And from that combined with other things, I think
forum posts and so on, you can start to figure out the names.
Yeah, I mean, in that case, it was specifically the Internet Movie Database where lots of Netflix
users publicly rate their movie, you know, their movie preferences. And so the anonymized data
and Netflix, when kind of, you know, 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 is in the sea of all Netflix users. There was just another paper on this in
science or nature of about a month ago that, you know, kind of 18 attributes. I mean, 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 a signature or, you know, fingerprint
and that if you can kind of, you know, do a join between that kind of data and allegedly
anonymize data, you have real trouble. So is there hope for any kind of privacy in the world where
a few likes can identify you? So there is differential privacy, right?
What is differential privacy? So differential privacy basically is a kind of alternate much
stronger notion of privacy than these anonymization ideas. And it, you know, it's a technical
definition, but like the spirit of it is we compare two alternate worlds. Okay, so let's
suppose I'm a researcher and I want to do, you know, I, 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,
you know, build a problem, you know, model predicting the probability that people have disease. So,
you know, this is the type of scientific research that we would like to be allowed to continue.
And in differential privacy, you act, 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, you know, it's two databases,
one with n records in it, and one with n minus one records in it. The n minus one 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 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, you know, like we discussed at some length, the study that,
you know, the in the 50s that was done that created the that 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, your insurer now
might have a higher posterior belief that you might have lung cancer and raise your premiums.
So you've suffered economic damage. But the point is, is that if the same analysis has been done
with all the other n minus one medical records and just yours missing, the outcome would have been
the same, your your data was an idiosyncratically 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.
But that's a very low value of harm. Yeah. So that's showing that very little harm is done.
Great. But how, what is the mechanism of differential privacy? So that's the kind of
beautiful statement of it. But what's the mechanism by which privacy is preserved?
Yeah. So it's basically by adding noise to computations, right? 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, you know,
to give a very concrete example, if I want to compute the average of a set of numbers, right,
the non private way of doing that is to take those numbers and average them and release
like a numerically precise value for the average. Okay. In differential privacy, you wouldn't do
that. You would first compute that average to numerical precisions. And then you'd add some
noise to it, right? You'd add some kind of zero mean, you know, Gaussian or exponential noise to
it. So that the actual value you output, right, is not the exact mean, but it'll be close to the mean.
But it'll be close. The noise that you add will sort of prove that nobody can kind of reverse
engineer any particular value that went into the average. So noise is the savior. How many
algorithms can be aided by adding noise? 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. And I've learned a tremendous amount working with him on it.
It's a pretty grown up field already. Yeah, but now it's 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 in showing 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 differentially privately. So pick your favorite machine learning algorithm, back
propagation and neural networks, you know, cart for decision trees, support vector machines,
boosting, you name it, as well as classic hypothesis testing and the like and statistics.
None of those algorithms are differentially 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, you know, we've become a,
you know, a scientific community very dependent on the use of machine learning and statistical
modeling and data analysis, we really do have a path to kind of provide privacy guarantees to
those methods. And so we can still, you know, enjoy the benefits of kind of the data science era
while providing, you know, rather robust privacy guarantees to individuals.
So perhaps a slightly crazy question, but if we take the ideas of differential privacy and
take it to the nature of truth that's being explored currently, so what's your most favorite
and least favorite food? Hmm. I'm not a real foodie. So I'm a big fan of spaghetti.
Spaghetti? Yeah. What do you really don't like?
I really don't like cauliflower. Well, I love cauliflower. Okay. But is one way to protect
your preference for spaghetti by having an information campaign, bloggers and so on,
of bots saying that you like cauliflower. So like this kind of the same kind of noise ideas.
I mean, if you think of in our politics today, there's this idea of Russia hacking our elections.
What's meant there, I believe, is bots spreading different kinds of information. Is that a kind
of privacy or is that too much of a stretch? 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.
Right. 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,
you know, 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 in a query into 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 the search engine. So, you know, it's like a weed and
chaff thing. So, you know, 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. Okay. So it's
the same kind of idea, kind of, you know, privacy by obfuscation. So I think that's an interesting
idea. 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 added to that. And so I think
there's sort of maybe even interesting technical questions around notions of privacy that are
appropriate where, you know, it's not that my data 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,
you know, there's a service that I really want to, you know, pay attention to my specific data,
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 are others as well.
So where do you think we'll land in this algorithm driven society in terms of privacy? So
sort of China, like Kaifu Lee describes, you know, it's collecting a lot of data on its citizens,
but in the best form, it's actually able to provide a lot of sort of protect human rights and
provide a lot of amazing services. And it's worse forms that can violate those human rights and
limit services. So where do you think we'll land? So algorithms are powerful when they use data.
So as a society, do you think we'll give over more data? Is it possible to protect the privacy of
that data? So I'm optimistic about the possibility of, you know, 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 like, 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, you know, 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, I'm going 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 and 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 we'll, you know, to kind of get more directly
answer 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, you know, provides better privacy overall and more
individual control of data and privacy. But, you know, 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. And I think you've mentioned the word control a lot. And I think giving people control,
that's something that people don't quite have in a lot of these algorithms. And that's a really
interesting idea of giving them control. Some of that is actually literally an interface design
question, sort of just enabling, because I think it's good for everybody to give users control.
It's not, it's not a, it's almost not a trade off, except that you have to hire people that are
good at interface design. Yeah, I mean, the other thing that has to be said, right, is that, you
know, it's a cliche, but, you know, we, as the users of many systems, platforms and apps, you
know, we are the product, we are not the customer. The customer are advertisers and our data is the
product. Okay. So it's one thing to kind of suggest more individual control of data and privacy and
uses. But this, you know, 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. So the idea of markets, you mentioned, by exposing
the economic model to the people, they will then become a market. They could be participants in
it. Participants in it. And, 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 them.
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 on others. And, you know, you know,
Google and Facebook are running, you know, they're pretty much their entire revenue stream is by
running two-sided markets between those parties, right? And so it's not a crazy idea that there
would be like a three-sided market or that, you know, that on one side of the market or the other,
we would have proxies representing our interest. It's not, you know, it's not a crazy idea,
but it would, it, it, it's not a crazy technical idea, but it would have
pretty extreme economic consequences. Speaking of markets, a lot of fascinating
aspects of this world arise not from individual humans, but from the interaction of human beings.
You've done a lot of work in game theory. First, can you say what is game theory and how does
it help us model and study it? Yeah. Game theory, of course, let us give credit where it's due.
They don't, you know, comes from the economist first and foremost, but as I mentioned before,
like, you know, 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, you know, game, game theory,
first and foremost, is a mathematical framework for reasoning about collective outcomes in systems
of interacting individuals. Yeah. You know, so you need at least two people to get started in game
theory. And many people are probably familiar with prisoner's 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 prisoner's
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. So what to you is the most beautiful idea that you've encountered
in game theory? There's a lot of them. I'm a big fan of the field. I mean, you know, I mean,
I mean, technical answers to that, of course, would include Nash's work just establishing
that, you know, there's a competitive equilibrium under very, very general circumstances, which
in many ways kind of put the field on a firm conceptual footing because if you don't have
equilibrium, it's kind of hard to ever reason about what might happen since, you know, there's
just no stability. So just the idea of the stability can emerge when there's multiple.
Or that I mean, not that it will necessarily emerge just that it's possible, right? I mean,
like the existence of equilibrium doesn't mean that sort of natural iterative behavior will
necessarily lead to it. In the real world. 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 real 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. So you kind of mentioned acting greedily can
somehow end up pretty good for everybody. Or pretty bad. Or pretty bad. It will end up stable.
Yeah. Right. And, you know, stability or equilibrium by itself is not necessarily either a good
thing or a bad thing. So what's the connection between machine learning and the ideas?
Well, I mean, I think 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.
Okay. So, you know, let me let me give I think the cleanest example, which is just driving apps,
navigation apps like, you know, Google Maps and Waze, where, you know, 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 hourly 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, you
know, the, the route 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 than it
would be under other solutions. And then you have to talk about what those other solutions might be
and what, what the algorithms to implement them are, which we do discuss in the kind of game theory
chapter of the book. But, but similarly, you know, on social media platforms or on Amazon,
you know, 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,
you know, 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
in which some or maybe even all of us might be better off. And then the connection to machine
learning, of course, is that in 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. Now, in terms of the stability and the promise of that, I have to ask just out of
curiosity, how stable are these mechanisms that you game theory is just the economists came up
with. And we all know that economists don't live in the real world. Just kidding. Sort of what's,
do you think when we look at the fact that we haven't blown ourselves up from the,
from a game theoretic concept of mutually shared destruction, what are the odds that we destroy
ourselves with nuclear weapons as one example of a stable game theoretic system?
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
US nuclear armament, disarmative detente, things like that. I'll be honest, as huge a fan as I am
of game theory and its kind of rich history, it still surprises me that you had people at the
Rand Corporation back in those days kind of drawing up two by two tables and one, the
row players, the US and the Cullen players, Russia, and that they were taking seriously.
I'm sure if I was there, maybe it wouldn't have seemed as naive as it does at the time.
It seems to have worked, which is why it seems naive.
Well, we're still here.
We're still here in that sense.
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, 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.
So you've worn many hats, one of which, the one that first caused me to become a big
fan of your work many years ago is algorithmic trading. So I have to just ask a question about
this because you have so much fascinating work there. In the 21st century, what role do you think
algorithms have in the space of trading, investment in the financial sector?
Yeah. It's a good question. I mean, in the time I've spent on Wall Street and in finance,
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 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. So first of all, there needed to be this era of automation where just financial
exchanges became largely electronic, which then enabled the possibility of trading becoming
more algorithmic because once exchanges are electronic, an algorithm can submit an order
through an API just as well as a human can do at a monitor. You can do it really quickly,
you can read all the data so it's okay. Yeah. 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 problems. 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 what we might consider difficult trades. 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, 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.
So this is an optimization problem. This is a control problem. And so
machines are better. We know how to design algorithms that are better at that kind of
thing than 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 mispricings between exchanges or within an asset itself,
or just predict directional movement of a stock because of the very, very low-level granular
buying and selling data in the exchange. Machines are good at this kind of stuff.
It's kind of like the mechanics of trading. What about the, can machines do long terms of
prediction? Yeah. So I think we are in an era where clearly there have been some very successful
quant hedge funds that are in what we would traditionally call still in the stat Arb regime.
Like so, you know, what's the stat Arb 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, buy 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,
you know, 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, you know, I think that
this was what its long-term value is, and it's far from that right now. And so I'm going to buy
some Apple or, you know, short some Apple, and I'm going to, I'm going to sit on that for 10 or 20
years. Okay. So when you're at that kind of time scale, or even more than just a few days,
these 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 understand human 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 time scales, 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 CEO gets caught in a scandal
or, you know, gets run over by a bus or something that can cause very sudden changes. But, you
know, 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, you know, all you need to know is as long as that's not going to happen
in the next 500 milliseconds, then, you know, my model is good. When you get to these longer
time scales, you really have to worry about that kind of stuff. And people in the machine
learning community are starting to think about this. We held a, we jointly sponsored a workshop
at Penn with the Federal Reserve Bank of Philadelphia a little more than a year ago
on, you know, I think the title is something like machine learning for macroeconomic prediction,
you know, macroeconomic referring specifically to these longer time scales. And, you know,
it was an interesting conference, but it, you know, it left me with greater confidence that
you 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, so somebody asked me like, well, whose job on Wall Street is safe
from the bots, I think people that are at that longer, you know, the 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, they're,
they're pretty safe for a while, as far as I can tell. So Warren Buffett's job is safe?
Yeah, I'm not seeing, you know, a robo Warren Buffett any time soon.
Give him comfort. Last question. If you could go back to,
if there's a day in your life, you could relive because it made you truly happy.
Maybe you outside the family. What day would it be? What, can you look back,
you remember just being profoundly transformed in some way or blissful?
I'll answer a slightly different question, which is like, what's a day in my life or my career
that was kind of a watershed moment? I went straight from undergrad to doctoral studies.
And, you know, that's not at all atypical. And I'm also from an academic family, like my,
my dad was a professor, my uncle on his side as a professor, both my grandfathers were professors.
All kinds of majors to philosophy. So yeah, kind of all over the map. Yeah. And I was a
grad student here just up the river at Harvard and came to study with Les Valley,
and which was a wonderful experience. But, you know, I remember my first year of graduate school.
I was generally pretty unhappy. And I was unhappy because, you know, 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, you know, that did all kinds of
different things. And, you know, Harvard's a much smaller institution than Berkeley.
And its computer science department, especially at that time, was, was a much smaller place than
it is now. And I suddenly just felt very, you know, like 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 in 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,
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 Common and kind of really just kind of chewing over what I wanted to do with my
life. And then I realized like, okay, you know, 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, you know, I'm going to stick,
I'm going to, you know, stick through it for the summer. And, you know, and that was very
formative because I went from kind of contemplating quitting to, you know, 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, it's just 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 figure 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.
And also just accepting the painful nature of that journey.
Yeah, exactly. Exactly. And in that moment said, I'm going to stick it out. Yeah, I'm going to
stick around for a while. Well, Michael, I've looked up to you work for a long time. It's
really nice to talk to you. Thank you so much. It's great to get back in touch with you too and
see how great you're doing as well. Thank you. Thanks a lot. Appreciate it.