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

“Thinking about trade offs between different types of quantities and resources and there being better and worse algorithms. Our book is about that part of algorithmic ethics that we know how to kind of put on that same kind of 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, for this lending model, the false rejection rate on black people and white people is within 3%. So we might call that a 97% ethical algorithm and a 100% ethical algorithm would mean that that difference is 0%.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 an algorithm that sweeps all the way through, finds the smallest number, puts it at the front, then sweeps through, again, finds the second smallest number. So we make the point that this is an algorithm and it's also a bad algorithm in the sense that it's quadratic rather than n log n, which we know is 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 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 better distributed implementations. And so the point is that already we're used to in computer science.”
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, let me riff for a second on your specific n log n example. So because early in the book, when we're just kind of trying to describe algorithms period, we say like, okay, what's an example of an algorithm?”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Now, of course, when that happens, we've seen what happens not too long ago. But the idea that it serves no Useful economic purpose under any circumstances is definitely not true.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and finance, I think the primary example I would give is leverage, right? So being allowed to borrow, to sort of use 10 times as much money as you've actually borrowed, 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 if the fact of the matter is that sort of on some particular time scale, you are holding positions that are very unlikely to your value at risk or variances or five percent, it kind of makes sense that you would be allowed to use a little bit more than you have because you have some confidence that you're not going to lose it all in a single day.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Papers, then nobody reads. You know, but when you're in that world, you come to see the value for it. But even though you might not be able to explain it to the person in the street.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Kind of have these social norms that 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 you people doing? Why are you paid so much in some cases a taxpayer expenses to do to”
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, or not that you drift, but even the things that. Don't make sense to the outside world don't seem unusual to you. So it's not sort of like a good or a bad thing. But for instance, in the world of finance, 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 activity exists at all. It just seems like completely useless and people just like, you know, pushing money around. And when you're in that world, right, and you learn more, your view does become more nuanced. You realize, okay, there is actually a function to this activity. And in some cases, you would conclude that actually if magically we could eradicate this activity tomorrow, it would come back because it actually is 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Maybe slippery slope is too strong of a word, but you're in some world where you're mainly around other people with the same kind of viewpoints and training and worldview as you. And I think that's more of a source of abuses of power than sort of there being good people and evil people. And it's somehow the evil people are the ones that somehow rise to power.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 clich ⁇ about absolute power corrupts absolutely. I mean, I think even short of that, having spent a lot of time on Wall Street and also in arenas very, very different from Wall Street like academia, one of the things I think I benefited from by moving between two very different worlds is you become aware that these worlds kind of develop their own social norms and they develop their own rationales for 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 professional cultures, for instance. And so then you're”
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'm an optimist. I tend to think that most people are good and want to do right and that deviations from that are kind of usually due to circumstance not to do to people being bad at heart.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think especially in fairness, 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. I mean, you've got these theorems saying here are three entirely reasonable, desirable notions of fairness. And here's a proof that you cannot simultaneously have all three of them. So I think we know that algorithmic fairness compared to algorithmic privacy is going to be kind of a harder problem. And it will have to revisit, I think, things that have been thought about by many generations of scholars before us. So it's very early days for fairness, I think.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 the biggest problems. And 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“Some sort of variant of Rawsey unfairness. What do you think? And I thought I was asking a yes or no question and I got back to your kind of classical philosopher's response. Well, it depends if you look at it this way, then you might conclude this. And that's when I realized that there was a real kind of rift between the ways philosophers and others had thought about things like fairness from sort of a humanitarian perspective and the way that you needed to think about it as a computer scientist if you were going to kind of implement actual algorithmic solutions.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“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 the rest of the society, the field technically advance and then quickly on the heels of that kind of the buzz kill of all of the antisocial 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source
“You've dug deep into my history, I see. 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.”
2019-11-19 · Lex Fridman Podcast · Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning · IDENTIFIED FROM THE TRANSCRIPT · source