Approaching fairness in machine learning
blog.mrtz.org
Approaching fairness in machine learning
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Re: Approaching fairness in machine learning
#2After studying this issue, and learning a lot more about learning and optimization, I've come to the conclusion that the best solution [1] is probably explicit racial/sexual/other special interest group quotas.
Specifically, we should train a classifier on non-Asian minorities. We should train a different classifier on everyone else. Then we should fill our quotas from the non-Asian minority pool and draw from the primary pool for the rest of the students.
As this blog post describes, no matter what you do you'll reduce accuracy. But every other fairness method I've seen reduces accuracy both across special interest groups and also within them. Quotas at least give you the best non-Asian minorities and also the best white/Asian students.
Quotas also have the benefit of being simple and transparent - any average Joe can figure out exactly what "fair" means, and it's also pretty transparent that some groups won't perform as well as others and why. In contrast, most of the more complex solutions obscure this fact.
[1] Here "best" is within the framework of requiring a corporatist spoils system. I don't actually favor such a system, but I'm taking the existence of such a spoils system as given.
Re: Approaching fairness in machine learning
#3Bayes and discrimination law doesn't seem like good partners.
Re: Approaching fairness in machine learning
#4Either you allow an algorithm to be ruthlessly fair, or you introduce bias and never get the problem solved correctly, because someone, somewhere, will still find a way to gripe about the amount of bias when, inevitably, it goes against them, or is perceived to be against them due to lack of knowledge. Then you wind up bikeshedding over the bias and not the actual problem.
Re: Approaching fairness in machine learning
#5Another recent paper on this topic: http://arxiv.org/pdf/1606.08813v3.pdf. It shows how naive lending algorithms can skew against minority groups simply because there is less data available about them, even if their expected repayment rate is the same.
It can be self-reinforcing. Imagine some new demographic group of customers appears, and without any data you make some loans to them. The actual repayment rate will be low, not because that group has a worse distribution than other groups, but simply because you couldn't identify the lowest-risk members. A simplistic ML model would conclude that the new group is more risky.
Of course, smart lenders understand that in order to develop a new customer demographic they need to experiment by lending, with the expectation that their first loans will have high losses, but that in the long run learning about how to identify the low-risk people from that demographic is worthwhile. And they correct for the fact that the first cohort was accepted blind when estimating overall risk for the group.
Re: Approaching fairness in machine learning
#6What is fairness but political accountability?
There is an old joke about how people use statistics like a drunk uses a lamp post: for support and not for illumination. Given this, we can expect people to use AI like everything else in statistics, to support the agenda of whoever is operating it while defraying negative personal accountability for the results, because artificial intelligence. It's just an obfuscated and sophisticated version of, "Computer says no."
The alternative is the near future headline, "AI confirms racists, sexists, on to something."
Re: Approaching fairness in machine learning
#7Another recent paper on this topic: http://arxiv.org/pdf/1606.08813v3.pdf . It shows how naive lending algorithms can skew against minority groups simply because there is less data available about them, even if their expected repayment rate is the same. It can be self-reinforcing. Imagine some new demographic group of customers appears, and without any data you make some loans to them. The actual repayment rate will…
Of course, this theory of discrimination is only applicable when minorities are fundamentally different from majorities. I.e., if the same ruleset is accurate for both whites and blacks (i.e., "I don't care about race, if he puts 20% down he's good"), this argument doesn't work at all - you can train your model on everyone and it'll work just fine.
However, if blacks and whites need to be treated fundamentally differently in order to make accurate loan decisions, then this argument applies. I.e., perhaps whites need a 20% downpayment for a loan to be financially a good risk but blacks need 40% (or vice versa).
I wonder how many people calling algorithms racist will endorse this conclusion. It sounds kind of...racist.
(Note that I don't use "racist" a synonym for "factually incorrect" or "we should not consider this idea", but merely "this sounds like the kind of thing a white nationalist might say, or Trump would be criticized for if he said".)
Re: Approaching fairness in machine learning
#8> As a result, the advertiser might have a much better understanding of who to target in the majority group, while essentially random guessing within the minority.
If this is the case, then it should be detected and ML should NOT be used for the minority class. There are many classifiers out there which work on one-class problems.
Re: Approaching fairness in machine learning
#9Another recent paper on this topic: http://arxiv.org/pdf/1606.08813v3.pdf . It shows how naive lending algorithms can skew against minority groups simply because there is less data available about them, even if their expected repayment rate is the same. It can be self-reinforcing. Imagine some new demographic group of customers appears, and without any data you make some loans to them. The actual repayment rate will…
Of course, this theory of discrimination is only applicable when minorities are fundamentally different from majorities . I.e., if the same ruleset is accurate for both whites and blacks (i.e., "I don't care about race, if he puts 20% down he's good"), this argument doesn't work at all - you can train your model on everyone and it'll work just fine. However, if blacks and whites need to be treated fundamentally diffe…
> a Trump voter
Is that really necessary?
Some of us are treating the political system like a blackbox, I'm just sending a different corrupt payload at it to see what the output is.
Re: Approaching fairness in machine learning
#10Earlier quoted context omitted.
Of course, this theory of discrimination is only applicable when minorities are fundamentally different from majorities . I.e., if the same ruleset is accurate for both whites and blacks (i.e., "I don't care about race, if he puts 20% down he's good"), this argument doesn't work at all - you can train your model on everyone and it'll work just fine. However, if blacks and whites need to be treated fundamentally diffe…
> a Trump voter Is that really necessary? Some of us are treating the political system like a blackbox, I'm just sending a different corrupt payload at it to see what the output is.
Perhaps not. I've altered it.