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Attacking discrimination with smarter machine learning

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Re: Attacking discrimination with smarter machine learning

#161

Post by tptacek »

tjic wrote: post #157
tptacek wrote: post #127

Earlier quoted context omitted.

No, he doesn't. He has a mixture of early Jensenist psychometric research that has been superseded, and neo-phrenologists like Rushton that have been discredited. But of course it's easy to drop little bombs like this into threads and put the onus on other people to explain the science. That's what makes it trolling. Whatever snappy response you have for this, please spare us. There is a reason "the subject of racial…

> But of course it's easy to... put the onus on other people to explain the science. > Whatever snappy response you have for this, please spare us. In two sentences you simultaneously attacked me for not providing cites and then told me not to provide cites. If you're going to Kafka trap people like this, where there is literally no way to respond to you without being attacked, that doesn't look like good faith debat…

That is correct: I do not want more cites. Please race troll elsewhere. I had to look up what "kafka trap" means; it appears to be an Eric S. Raymond term. Let me suggest Eric Raymond's blog as a place more welcoming to your urge to litigate the merits of different races.

Re: Attacking discrimination with smarter machine learning

#162

Post by Retric »

weberc2 wrote: post #155
Retric wrote: post #106

Earlier quoted context omitted.

Sounds like you are just avoiding uncomfortable questions. However, most people will make assumption on race and class from things like PCP even though zero information about that in my example.

No, I just don't understand your question. Do you want me to make a crappy guess based on the crappy data you provided? If so, I don't understand why... Judging by subthreads, you just want to say "Ha! Your crappy guess was wrong!". If you want me to pretend that I'm somehow involved in the processing of this case, but I'm restricted to only the crappy data provided, I wouldn't "assume" anything--I would say I don't…

> Do you want me to make a crappy guess based on the crappy data you provided?

No, I had zero problem with your response (assuming it was genuine). As I said, it sounds like you are avoiding the question, however avoiding making those associations is the correct response.

Anyway, I was not asking if you thought they where guilty or anything. This was a friend of mine and he had zero problem admitting what happened.

Re: Attacking discrimination with smarter machine learning

#163

Post by tboyd47 »

deong wrote: post #75
Scea91 wrote: post #25

>[...] concept called equal opportunity. Here, the constraint is that of the people who can pay back a loan, the same fraction in each group should actually be granted a loan. This does not seem fair to me, because if this is applied then your race (group) would determine your credit score threshold which feels discriminatory to me. I feel that, by definition, it is not discriminatory only if none of your attributes…

But how do you do that? Race is baked into a lot of the attributes your classifier is going to find informative. Is someone a good credit risk? To decide, you look at features like past payment history, available balance, zip code, etc. Past payment history: if you're black, prior discriminatory behaviors may have limited your ability to open credit accounts, and thus you have less history to go on. Available balance…

You'd be surprised. If you've ever racially identified on a standardized test, guess what, you've just set the is_black feature.

Re: Attacking discrimination with smarter machine learning

#164

Post by tptacek »

yummyfajitas wrote: post #142
tptacek wrote: post #24

Earlier quoted context omitted.

The difference with machine learning is that the model isn't designed by humans, through an actuarial process we can keep our brains wrapped around. It's a black box. We are OK attributing "crash risk" to young male drivers, because we can observe both that they are as a cohort statistically likely to crash and also understand why that would be the case. On the other hand, we're not comfortable with the idea that a c…

No, bogus correlations and inaccurate conclusions are explicitly NOT the computer science problem being worked on here. You are trying to frame the problem as algorithms wrongly classifying people. That's just a standard statistics problem (called "overfitting") and has nothing to do with fairness. The problem being worked on here is "what if Armenians shouldn't get car loans because they don't pay them back as much…

"We proposed a fairness measure that accomplishes two important desiderata. First, it remedies the main conceptual shortcomings of demographic parity as a fairness notion. Second, it is fully aligned with the central goal of supervised machine learning, that is, to build higher accuracy classifiers."

Your dispute may be with the authors.

Re: Attacking discrimination with smarter machine learning

#165

Post by tptacek »

yummyfajitas wrote: post #151
tptacek wrote: post #122

Earlier quoted context omitted.

Isn't that the kind of work we're commenting on with this story?

No, it's not at all the same. Those articles are about eliminating overfitting and improving accuracy. This article is about how much fairness constraints will reduce your accuracy/objective function.

Did you read the paper, or just the web page with the simulator? The loan simulator is a motivating example, not the entirety of the work. The work is a comparison between the basic approach of demographic parity and the authors contributions, which seeks to minimize the expense you seem to be talking about.

Re: Attacking discrimination with smarter machine learning

#166

Post by vorotato »

idanoeman wrote: post #140
tptacek wrote: post #116

Earlier quoted context omitted.

Generally we're most of us in society pretty comfortable with paying for the continuation of the species. There are exceptions to every rule, of course; some of us (not including me) are unhappy we pay to pave the streets, too.

I don't think that we need to be subsidizing babies when the world population is 7.4 billion.

That argument was just as valid when there were only 2000 people.

Re: Attacking discrimination with smarter machine learning

#167

Post by yummyfajitas »

tptacek wrote: post #164
yummyfajitas wrote: post #142

Earlier quoted context omitted.

No, bogus correlations and inaccurate conclusions are explicitly NOT the computer science problem being worked on here. You are trying to frame the problem as algorithms wrongly classifying people. That's just a standard statistics problem (called "overfitting") and has nothing to do with fairness. The problem being worked on here is "what if Armenians shouldn't get car loans because they don't pay them back as much…

"We proposed a fairness measure that accomplishes two important desiderata. First, it remedies the main conceptual shortcomings of demographic parity as a fairness notion. Second, it is fully aligned with the central goal of supervised machine learning, that is, to build higher accuracy classifiers." Your dispute may be with the authors.

No, you are misunderstanding things. They aren't improving accuracy, reducing overfitting, or improving generalizability. If you think I'm wrong, feel free to cite the part of the paper where they claim to build a better FICO score (to go with their example).

Re: Attacking discrimination with smarter machine learning

#168

Post by yummyfajitas »

tptacek wrote: post #165
yummyfajitas wrote: post #151

Earlier quoted context omitted.

No, it's not at all the same. Those articles are about eliminating overfitting and improving accuracy. This article is about how much fairness constraints will reduce your accuracy/objective function.

Did you read the paper, or just the web page with the simulator? The loan simulator is a motivating example, not the entirety of the work. The work is a comparison between the basic approach of demographic parity and the authors contributions, which seeks to minimize the expense you seem to be talking about.

I read the paper a month ago, and I'm quite familiar with this field for my own reasons [1]. They attempt to minimize the expense I'm talking about. They don't reduce overfitting, improve accuracy, or even make any changes to the underlying predictive algorithm.

The simulator is a great illustration of exactly what they did; the entirety of the work is generalizing that to arbitrary predictors (subject to a few conditions) and bounding the accuracy penalty.

Feel free to cite the theorem improving accuracy if you disagree.

[1] One idea I'm kicking around is the following. Banks/others are legally required to issue bad loans for fairness. I suspect there is a lot of money to be made hacking this, I just haven't figured out how yet.

Re: Attacking discrimination with smarter machine learning

#169

Post by [deleted] »

qb45 wrote: post #141
idanoeman wrote: post #140

Earlier quoted context omitted.

I don't think that we need to be subsidizing babies when the world population is 7.4 billion.

The problem is that retirement is a Ponzi scheme and it's not clear if the current 3rd world children will care (or even have the means to care) about our 1st world problems in the future.

[deleted]

Re: Attacking discrimination with smarter machine learning

#170

Post by eli_gottlieb »

lifeisstillgood wrote: post #96

sorry, just checking - young black men are statisically less likely to rape than equivalent white (young men) That is surprising. And given this discussion about only wanting stats that have a valid explanation as well as fitting the facts, is interesting.

>That is surprising.

Have you seen statistics to the contrary? Why would it be surprising if you don't have an informed prior in the opposite direction?

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