YouSaid · the spoken record

Michael Kearns

lines on the record: 116
first: 2019-11-19
most recent: 2019-11-19
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Giving input on that decision. I just don't like the cost benefit analysis to the field of kind of going there right now just doesn't seem worth it to me.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 n features. And you can use algorithmic techniques to sort of try to find the subgroups on which you're discriminating the most and try to fix that. That's actually kind of the form of one of the algorithms we developed for this Fairness Gerrymandering problem. But partly because of our technology, sort of our scientific ignorance on these topics right now and also partly just because these topics are so loaded emotionally for people that I just don't see the value. I mean, again, never say never, but I just don't think we're at a moment where it's a great time for computer scientists to be rolling out the idea like, hey, not only have we kind of figured fairness out, but we think the algorithms should start deciding what's fair.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“More you know about a field, the more aware you are of its limitations. And so I'm pretty leery of sort of trying, you know, there's so much we don't all, we already don't know in fairness, even when we're the ones picking the fairness definitions and comparing alternatives and thinking about the tensions between different definitions, that the idea of kind of letting the algorithm start exploring as well, I definitely think this is a much narrower statement. I definitely think that kind of algorithmic auditing for different types of unfairness, right?”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“I haven't thought about that question specifically in the context of fairness. I definitely would agree with that statement in the large, right? I mean, I am, you know, 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 mean, I don't think we're kind of... Notably closer to general artificial intelligence now than we were when I started my career. I mean, there's been progress. And I do think that we are kind of as a community maybe looking a bit where the light is, but the light is shining pretty bright there right now, and we're finding a lot of stuff. So I don't want to like argue with the progress that's been made in areas like deep learning, for example.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Or that same person could say that'll be long term healthy for the platform. For the platform's influence on society outside of the platform, right? And it's easy for me to sit here and say these things. But conceptually, I do not think that these are kind of totally or should they be kind of completely alien ideas. You could try things like this, and it wouldn't be, you know, we wouldn't have to invent entirely new science to do it because if we're 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 exactly the same model that tells that dictates how to make me really happy also tells how to make me as unhappy as possible as well.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“I think no thanks would be my first response There are many aspects of being the head of the entire company that are kind of entirely exogenous to many of the things that we're discussing here. And so I don't really think I would need to be CEO of Facebook to kind of implement 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 see more stuff in their news feed that is not entirely kind of chosen to optimize for their particular interests. Beliefs, et cetera.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“That's right. So I'm not claiming that doing something different will immediately make it apparent that this is a good thing for society. And in particular, I mean, I think one way of thinking about where we are on some of these social media platforms is that it kind of feels a bit like we're in a bad equilibrium, 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, why would I want to see things in my newsfeed that I found irrelevant, offensive, you know, or the like? Okay. But maybe by all of us, having these platforms myopically optimized in our interests, we have reached a collective outcome as a society that we're unhappy with in different ways. Let's say with respect to things like political.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 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 that anytime you know how to optimize for something, almost by definition, that solution tells you how not to optimize for it or to do something different.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, in the short term, right? And again, 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 vested in the platform and enjoying it and finding it useful, interesting, and or productive. But my point is that the idea that”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, it's definitely possible to do different, right? And again, 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. For sure.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“You could even imagine users being able to control this, you know, just like everybody gets a slider and that slider says, 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? It's almost like an exploration button”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“As you know, these algorithms have models, and they kind of place people in some kind of metric space and they place content in that space, and they sort of know the extent to which I have an affinity for a particular type of content. And by the same token, they also probably have that same model probably gives you a good idea of the stuff I'm likely to violently disagree with or be offended by. 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 they don't like or that's a little bit further away.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Are there algorithmic remedies to these kinds of things? And again, these are big problems that are not going to be solved with somebody going in and changing a few lines of code somewhere in a social media platform. But I do think in many ways there are definitely ways of making things better. I mean, like an obvious recommendation that we make at some point in the book is, look, to the extent that we think that machine learning applied for personalization purposes in things like news feed, you know, or other platforms has led to polarization and intolerance of opposing viewpoints.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Part by the behavior of the users themselves and how the users decide to adopt them and how to use them. And so I'm kind of like, who really knew that until we saw it happen, who knew that these things might be able to influence the outcome of elections? Who knew that they might polarize political discourse because of the ability to decide who you interact with on the platform and also with the platform naturally using machine learning to optimize for your own interest that they would further isolate us from each other and feed us all basically just the stuff that we already agreed with? And so I think we've come to that outcome, I think, largely, but I think it's. Something that we all learned together, including the companies as these things happen. Now, you asked, like, well,”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 these platforms, right? They don't operate in isolation. So like unlike the other topics we're discussing, like fairness and privacy, those are really cases where algorithms can operate on your data and make decisions about you. And you're not even aware of it. Things like Facebook and Twitter, 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 way.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“People really wonder why would anybody want to spend time doing that? 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, well, 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”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Look, 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah. I mean, especially these days where people are, you know, concerned about the robots becoming our overlords, the idea that the robots would also sort of develop their own social norms as 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 kind of living in a world where in many ways computer scientists have made some decisions that have fundamentally changed the nature of our society and democracy and sort of civil discourse and deliberation in ways that I think most people generally feel are bad these days, right?”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, for the most part in the book, a point that we try to take some pains to make is that 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“A valid counter argument is to say, well, no, you don't have to, there's no notion of attention 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 data set. Or you could train a separate model on those subgroups and have multiple models. The point I think we would, you know, we tried to make in the book is that those things have cost too, right? Going out and gathering more data on groups that are relatively rare compared to your plurality or majority group that it may not cost you in the accuracy of the model, but it's going to cost the company developing this model more money.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“And I'm training the model to maximize the overall accuracy on my training data set, that the model can reduce its error most by getting things right on the white males that constitute the majority of the data set, 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“The many ways in which bias or unfairness can come into algorithms, especially in the machine learning era, right? And, you know, I think many of your viewers have probably heard these examples before. But let's say I'm building a face recognition system. 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 data set was primarily white males.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 I mean, do you think just it might be helpful just to demystify a little bit about?”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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. 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Just like say predictive error, the probability or frequency with which you release somebody on parole who then goes on to recommit a violent crime or keep incarcerated somebody who would not have recommitted a violent crime.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Specific trade up I talked about just in order to make things very concrete was between Numerical error and some numerical measure of unfairness.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Telling a criminal jurisdiction, look, if you're concerned about racial fairness, but you're also concerned about accuracy, you want to release on parole people that are not going to recommit a violent crime and you don't want to release the ones who are. So, you know, that's accuracy. But if you also care about those, the mistakes you make not being disproportionately on one racial group or another, you can show this curve. I'm hoping that in the near future it'll be possible to explain these curves to non-technical people that are the ones that have to make the decision where do we want to be on this curve? Like what are the relative merits or value of having lower error versus lower unfairness? You know, that's not something computer scientists should be deciding for society.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Face. If I want to go from 17% error down to 16% error, what will be the increase in unfairness that I experience as a result of that? And so this curve kind of specifies the undominated models, models that are off that curve can be strictly improved in one or both dimensions. You can either make the air better or the unfairness better or both. And I think our view is that not only are these objects, these Pareto curves, efficient frontiers, as you might call them, Not only are they valuable scientific objects, I actually think that 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 your algorithm now has a knob that basically says, how strongly do I want to enforce fairness? And the less unfairness, you know, if the two axes are air and unfairness, we'd like to be at zero, zero. We'd like zero error and unfairness simultaneously. Anybody who works in machine learning knows that you're generally not going to get to zero error period without any fairness constraint whatsoever. So that's not going to happen. But in general, you'll get this kind of convex curve that specifies the numerical trade-off.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“No, we want to touch that, and we do touch it. So, I mean, just again to make sure I'm not promising 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 from data to kind of say like, okay, your false rejection rate on women is this much higher than it is on men. But once you also put the fairness into your objective function, I mean, I think the table that you're talking about is what we would call the Pareto curve, right? You can literally trace out”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, I agree. And so it's interesting to be a researcher trying to do, for the most part, technical algorithmic work, but Aaron and I both quickly learned you cannot do that and then go out and talk about it and expect people to take it seriously if you're unwilling to engage in these broader debates that are entirely extra algorithmic, right? They're not about 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?”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“About FAR 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 predicting failures to appear or making parole decisions or the like, they'll point out that, well, these definitions of fairness are all about fairness for the criminals. And what about fairness for the victims? So when I basically say something like, well, the false incarceration rate for black people and white people needs to be roughly the same. There's no mention of potential victims of criminals in such a fairness definition.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“And you don't have these somehow the discussions of fairness don't become politicized along other dimensions like race and about gender and whether we should, and you quickly find yourselves kind of revisiting topics that have been unresolved forever, like affirmative action, right? Why are you protecting, you know, some people will say, why are you protecting this particular racial group? And others will say, well, we need to do that as a matter of retribution. Other people will say it's a matter of economic opportunity. 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 in that as soon as you start talking about it, you inevitably have to participate in debate.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, of course, 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. Fairness is an interesting topic in that having worked in and 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“But that 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 individual end of the spectrum.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“We don't know how to provide sensible, tractable, realistic fairness guarantees at the individual level. But maybe we could start creeping towards that by dealing with more refined subgroups. I mean, we gave a name to this phenomenon where you 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're giving some guarantee at the aggregate level.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 from everybody else in the world. And so it is possible to put these incredibly coarse ways of thinking about fairness and these very, very individualistic specific ways on a common scale. And one of the things we've worked on from a research perspective is so we sort of know how to, in relative terms, we know how to provide fairness guarantees at the coarsest end of the scale.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 a 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“But at the end of the day, an individual you can think of as a combination of all of their attributes, right? They're a member of a racial group, they have a gender, they have an age, and many other demographic properties that are not biological but that are still 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,”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 is a few groups like different racial groups, maybe gender groups, maybe age, what have you. And let's make sure that for none of these groups do we have a false negative rate which is much higher than any other one of these groups. So these are kind of classic group aggregate notions of fairness.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 computer scientists have made significant contributions to that field, even though we kind of embarrassed ourselves for the first decade. So I think if computer scientists are going to start engaging in kind of psychology, human subjects type of research, we should expect to be embarrassing ourselves for a good 10 years or so and then hope that it turns out as well as some other areas that we've waded into.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 experience. 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 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 some amount of time. Like, you know, I think reinforcement learning.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“And as far as I can tell, everybody agrees that deep learning or at least the outputs of deep learning are not very understandable. And people might agree that sparse linear models with integer coefficients are more understandable. But nobody's really asked people. There's very little literature on 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Don't know from a subjective standpoint, like what people really think is fair. We just started doing a little bit of work in our group at actually doing kind of human subject. Experiments in which we ask people about, we ask them questions about fairness, we survey them, 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? 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 kind of scholars who think about fairness 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, I think we're not even at the point where we can sensibly think about that. So, first of all, we're talking here both about fairness applied at the group level, which is a relatively weak thing, but it's better than nothing. And also the more ambitious thing of trying to give some individual promises. But even that doesn't incorporate, I think, something that you're hinting at here is what a child might 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 thinks like, okay, you know, I think here's a technical definition of fairness that I think people should want or that they should 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”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Incorrectly denied alone, all of these definitions basically say like, well, your compensation is the knowledge that we are also falsely denying loans to other people in other groups at the same rate that we're doing it to you. And so there is actually this interesting, even technical tension in the field right now between these sort of group notions of fairness. And notions of fairness that might actually feel like real fairness to individuals, right? They might really feel like their particular interests are being protected or thought about by the algorithm rather than just the groups that they happen to be members of.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Falsely rejecting a creditworthy individual, sort of a false negative, is the real harm and that false positives, i.e. people that are not creditworthy 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. 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 are an individual who is falsely denied alone.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“First of all, I certainly didn't mean to give the impression that you can kind of measure memory speed tradeoffs and that there's a complete mapping from that onto kind of fairness, for instance, or ethics and accuracy, for example. In the type of fairness definitions that are largely the objects of study today and starting to be deployed, you as the user of the definitions, you need to make some hard decisions before you even get to the point of designing fair algorithms. One of them, for instance, is deciding who 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,”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source