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Attacking discrimination with smarter machine learning

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Re: Attacking discrimination with smarter machine learning

#181

Post by zepto »

weberc2 wrote: post #156
zepto wrote: post #68

Earlier quoted context omitted.

Nobody is talking about a conspiracy - racism is about bias.

Fair enough. It would be unlikely that millions of police would be spontaneously biased against African Americans, particularly when you factor in that those officers most likely to use excessive force against African Americans are themselves African American.

Nobody is talking about the bias being spontaneous. It is chronic and cultural.

Do you have anything to back up your claim that African American officers are more likely to use force against African Americans than white officers?

Re: Attacking discrimination with smarter machine learning

#182

Post by lifeisstillgood »

dang wrote: post #143
chronic6l wrote: post #113

Blacks (or African Americans) are less intelligent than other ethnicities, in average. We have information that satisfies your two criteria of supporting class information. Yet people on HN (liberals/progressives) will get upset with this fact. Supporting classes: 1. Through employment, high school graduation, incarceration, and homicide rates. 2. Due to black cultural, male macho-independence, discrimination from no…

Racewar threads are off topic on the grounds of ultimate tediousness. All it leads to is flamewars, and no flamewar will ever resolve any of it. Meanwhile it poisons what we do care about: intellectual curiosity, and civil, substantive discussion. Gwern has a nice bit somewhere about how you can either devote your entire life to this stuff or give up. Most of us, the vast majority, have given up. The rest of you true…

Edit: ok, you probably have loads of comments to moderate and do this a lot, but this is a big shock to me - being accused of racewar is a bit of a "pull up".

Ok, I can see how we got here - next time I will just say "citations" but now that we have broached the subject that I am being monitored and have breeched guidelines - can you give specifics so I can learn something. If I ever do learn?

Please respond assuming my comment was not intended to troll, which it was not, even if it was badly worded.

Ps what's gwern?

PPS Stone tablets with science? I don't get the conjunction but it is really dismissive - just how often do you have to do this removal thing.

Re: Attacking discrimination with smarter machine learning

#184

Post by lifeisstillgood »

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.

Ok, this has been a horrific morning - that first paragraph should have had ??!!!! or similar at the end. Which I hope turns it into more of a challenge than the total dick move it looks like.

I should have gone with "citation please"

Anyway, I have written up a sort of overly long comment which won't fit in 2000 chars so I can't submit it and so this is the link

http://www.mikadosoftware.com/articles/HNdisaster

I apologise for thinking this was not a slippery slope and for not reviewing my text for misunderstanding. And I apologise for putting a piece of text up there that is so blatantly... horrific. It's awful to have that in my permanent history even if I know how it was a mistake.

As it says in the article I am going to go and have some reflection time.

What a cock up.

See you in a few weeks.

Re: Attacking discrimination with smarter machine learning

#185

Post by qb45 »

yummyfajitas wrote: post #154
qb45 wrote: post #153

Earlier quoted context omitted.

I think what the research itself (as opposed to politics surrounding it) was about is summed up in the last paragraph: A key result in the paper by Hardt, Price, and Srebro shows that—given essentially any scoring system—it's possible to efficiently find thresholds that meet any of these criteria. The crucial thing is that this "credit score" doesn't actually predict defaults quite well, because blues who are 50% lik…

Let me repeat what I said: Go play with the simulation to see. The various fairness criteria all achieve lower than maximal profits. The best predictor is one which is explicitly discriminatory based on race: it takes both FICO score and race into account. There are worse predictors which also discriminate explicitly based on race, but in some "fair" way. Finally, the worst predictor throws away directly relevant rac…

I think you are being deceived by your real-world presumptions about the fictional example somewhat unfortunately and misleadingly engineered by Google. From your previous post:

> The problem being worked on here is "what if Armenians shouldn't get car loans because they don't pay them back as much as other groups?" I.e., algorithms rightly classifying people leads to results that we believe are "unfair".

No. If you stop playing with simulation and look at the input data, Armenians don't pay back less. They pay exactly as much as Iranians except that Iranians consistently have higher FICO scores because Iran infiltrated FICO with their suckxnet(TM) worm which replaces R binaries with hacked versions. Or something like that :)

The general abstract idea is: you have some input "score" which is know to inaccurately predict the outcome, use the input score and measurements of its biases to produce more accurate prediction than naive threshold classifier would.

And yes, the other guys talking about women's healthcare costing more or men causing more traffic accidents got it wrong too. I somewhat arbitrarily responded to you because you said something about "the real computer science problem being worked on here" and then continued to talk about other things like everybody else.

Re: Attacking discrimination with smarter machine learning

#186

Post by alvarosm »

dang wrote: post #148
alvarosm wrote: post #60

Earlier quoted context omitted.

Any SJW care to explain why I'm wrong instead of downvoting? thanks! this is a lot of fun :D

Since you've ignored our repeated requests to stop breaking the HN guidelines, we've banned your account. If you don't want to be banned, you're welcome to email hn@ycombinator.com. We're happy to unban people if they give us reason to believe they'll only post civil, substantive comments in the future.

Thank you for making the Internet a safe space! I don't know what the world would be like without the contributions of loyal zealots like you.

Re: Attacking discrimination with smarter machine learning

#187

Post by yummyfajitas »

qb45 wrote: post #185
yummyfajitas wrote: post #154

Earlier quoted context omitted.

Let me repeat what I said: Go play with the simulation to see. The various fairness criteria all achieve lower than maximal profits. The best predictor is one which is explicitly discriminatory based on race: it takes both FICO score and race into account. There are worse predictors which also discriminate explicitly based on race, but in some "fair" way. Finally, the worst predictor throws away directly relevant rac…

I think you are being deceived by your real-world presumptions about the fictional example somewhat unfortunately and misleadingly engineered by Google. From your previous post: > The problem being worked on here is "what if Armenians shouldn't get car loans because they don't pay them back as much as other groups?" I.e., algorithms rightly classifying people leads to results that we believe are "unfair". No. If you…

The general abstract idea is: you have some input "score" which is know to inaccurately predict the outcome, use the input score and measurements of its biases to produce more accurate prediction than naive threshold classifier would.

Why don't you quote the place in the paper where they make accuracy go up, fix overfitting, or build an improved risk score? Or even just quote a place in the paper where the risk score is treated as anything other than an accurate black box?

They pay exactly as much as Iranians except that Iranians consistently have higher FICO scores because Iran infiltrated FICO with their suckxnet(TM) worm which replaces R binaries with hacked versions.

In the example provided in the paper (see Fig 7) that's explicitly NOT true. Blacks pay back their loans a lot less than asians/whites holding FICO fixed. For example, at a FICO score of 500, blacks pay back their loans 10% of the time while Asians do about 40%.

I.e., blacks have consistently lower FICO scores because they don't pay back their loans. Further, FICO score is biased in favor of blacks. If we made it more accurate we'd be actively discriminating against blacks. For example, a black person with a financial situation reflecting a FICO of 500 would have their FICO score lowered to approx 450 to reflect their higher default rate.

Did you even read the paper, or the linked article?

Re: Attacking discrimination with smarter machine learning

#188

Post by [deleted] »

dang wrote: post #180
weberc2 wrote: post #66

I agree that this should be a possibility, while I think the odds that millions of law enforcement officers and criminal justice faculty would spontaneously conspire against a particular race, I also think it's more likely than some genetic predisposition toward crime (i.e., the first option).

There are so many places where this thread took a dismal turn that I suppose it's hopeless to even try pruning it, but since this is one of those places, we detached it from https://news.ycombinator.com/item?id=13005641 and marked it off-topic.

[deleted]

Re: Attacking discrimination with smarter machine learning

#189

Post by qb45 »

yummyfajitas wrote: post #187
qb45 wrote: post #185

Earlier quoted context omitted.

I think you are being deceived by your real-world presumptions about the fictional example somewhat unfortunately and misleadingly engineered by Google. From your previous post: > The problem being worked on here is "what if Armenians shouldn't get car loans because they don't pay them back as much as other groups?" I.e., algorithms rightly classifying people leads to results that we believe are "unfair". No. If you…

The general abstract idea is: you have some input "score" which is know to inaccurately predict the outcome, use the input score and measurements of its biases to produce more accurate prediction than naive threshold classifier would. Why don't you quote the place in the paper where they make accuracy go up, fix overfitting, or build an improved risk score? Or even just quote a place in the paper where the risk score…

OK, I see what's the problem. By "higher score" I meant "higher score than the other group for given default risk", not "higher score than the other group on average". So, by my nomenclature, fig. 7.1 shows Blacks having "higher" (call it inflated) FICO scores because for every N an N% risk Black scores higher than an N% risk Asian. At the same time fig. 7.2 shows Blacks having lower scores in general, which must be because they default more, as you observed.

I think we can agree that using the same cutoff for both races gives less accuracy than using higher cutoff for Blacks and lower for Asians, for some values of "higher" and "lower". You are right that "race discrimination is the best way to make money", but at the same time I think I'm right that this happens because FICO score already is racist to begin with, which is the "garbage in" I talked about.

Re: Attacking discrimination with smarter machine learning

#190

Post by witty_username »

mcbits wrote: post #160
witty_username wrote: post #98

Earlier quoted context omitted.

Price of insurance is not a right. It is not fair to normalize the price. It penalizes people who are healthy and good drivers (for example not smoking and speeding).

The whole purpose of insurance is to normalize the price. Without insurance, people who take the most risks with their health/driving would be more likely to pay (or at least owe) millions, while the health-conscious and risk-averse would generally pay much closer to $0 (until they get old or unlucky). It's interesting that insurance premiums can act as behavioral incentives in some cases, but I doubt it has ever led…

No, the purpose of insurance is to hedge the risks (i.e. one would prefer to take 0$ than to take a 50-50 bet on 1000$).

For example, the price of bond insurance is not normalized between bonds with different ratings.

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