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Approaching fairness in machine learning

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Re: Approaching fairness in machine learning

#51

Post by mklim »

wyager wrote: post #49
mklim wrote: post #47

Earlier quoted context omitted.

>If group X is as effective as group Y but you can get away with paying them 20% less, why would you NOT hire group X? Hypothetical possibility: members of group X are not perceived as 100% as effective as group Y because of pervasive bias by the employers that assumes their incompetence. They are generally perceived to be 80% as effective as a standard Y member despite actual 100% performance, and paid accordingly.…

Ideally, management would just look at the numbers at some level and figure out if there was some measurable pay disparity they could arbitrage and make money off of. I'm sure some companies have. This is a benefit of impersonal, faceless corporate structures; they don't have human qualities like biologically motivated bias in judgement. On the other hand, they don't have qualities like empathy either, so it's not cl…

Possible. But there's still potential problems with that, tying in to the article's main issue of potential feedback loops/bias in ML algorithms. Let's assume pay is correlated to perf/job title, and members of group X are consistently rated 80% of what a member of group Y would earn for identical performance by unintentionally biased managers. Let's assume that they're all similarly 80% as likely to be promoted given identical performance. Anyone looking at the data would find that pay for X and Y members is fair given their perf scores/job titles, and that members of X tend to underperform compared to Y. They could suspect bias in perf from that, or they could conclude that members of X are fairly paid but statistically underperforming. An objective evaluation of a biased/unfair dataset doesn't necessarily guarantee a fair/objective outcome.

Re: Approaching fairness in machine learning

#52

Post by sevenless »

Eridrus wrote: post #20

The biggest issues of bias/fairness in ML are not to do with the algorithms or results, but the underlying data. A trivial example would be: what if you trained a classifier to predict whether a person would be re-arrested before they went to trial? Some communities are policed more heavily so you would tend towards reinforcing the bias that exists and provide more ammunition to those arguing for further bias in the…

One of the great things about programming a computer to do something is that it forces you to expose and make explicit every hidden assumption. "Computers are very stupid: they only do exactly what you tell them to do."

With biases about people based on immutable characteristics (sex and race), we need to be clear about why stereotypes are bad and what we hope to achieve by eliminating stereotype-based reasoning. There is a great deal of hypocrisy and pretense around this subject, but only by being explicit and unapologetic can we explain to a computer what it is we want to achieve.

Stereotypes are not bad because they are false. Many stereotypes, even negative and unpleasant ones about vulnerable minorities, are statistically true at this time. A stereotype is nothing but a certain kind of model, and indeed, models built on sex and race stereotypes may perform better than those that aren't.

Nonetheless, we have strong norms against using stereotypes in law, public life and employment, because the outcome of such reasoning would be intrinsically unjust, and because of a long history of political struggle against a society, that explicitly discriminated on the basis of race, sex, homosexuality and so on. Conservatives will disagree with these premises, but we implicitly reject a conservative, discriminatory vision of society. Rejecting oppressive and unjust stereotypes is an unavoidably political act.

We recognize that

[0] It is a category error to treat humans like other kinds of objects which can be measured, because human beings are intelligent and can alter their behavior. Telling humans that science has discovered certain facts about their behavior may well change their behavior, or even re-order society around these new 'facts'. There is no neutral ground; doing statistics on humans has ethical implications. An awful example of this was the eugenic movement that inspired the Nazis.

[1] Stereotypes can reify themselves. A society which treats women as less than men will end up as a society where women are less than men, and are systematically harmed - they will be less educated, and will get treated as less intelligent. This is a kind of positive feedback loop between the widespread endorsement of a stereotype and its being 'confirmed as true'.

[2] It's intrinsically unjust to judge individuals, especially in a negative way, based on the behavior of others. This is a matter of justice, and it overrides considerations of predictive accuracy.

[3] We live in a society that is still unjust, racist, sexist and so on. We want a society where fair and equal opportunities are given to everyone, and where everyone has a chance to escape from negative stereotypes. This overrides efficiency. In that sense, we can hope to change the nature of society by changing the nature of claims that are widely held as 'truth'.

These are sophisticated premises for rejecting stereotype based reasoning, and they come from rejecting the society-wide outcomes of treating stereotypes as truth. We know what these effects are, because we know what a discriminatory society looks like. But this is the kind of reasoning we can use to build non-discriminatory, socially just models that do not harm people simply for being who they are.

Re: Approaching fairness in machine learning

#53

Post by closed »

yummyfajitas wrote: post #42
closed wrote: post #35

Earlier quoted context omitted.

It is a bias if you calculate the cost to people taking out loans, based on color. Green people will pay a higher cost, even though in the ground-truth model their race is not directly related to loan repayment. For example, if only blue people in Idaho fail to repay loans, green people will still absorb a greater cost in the multiple regression case above (in the sense that they are more likely to be penalized for b…

Yes, if it's actually (blue & Idaho) ~> default, and your model ignores blue, then the greens will pay a higher cost. If color is redundantly encoded then your model can partially fix this and penalize the blue's in Idaho. Do you consider this situation unjust? If so, you might be unhappy to learn that the entire goal of the field of algorithmic fairness is to do something along these lines.

> Available data on blacks specifically is completely irrelevant if blacks and whites aren't fundamentally different. The white model will generalize.

I should have been more clear that I was responding to this part of your comment. That even if blacks and whites aren't fundamentally different (in the sense that your race does not directly cause an outcome of interest) you can produce biases that are essentially a misatrribution about the relationship between race and that outcome. Worse, if there_is_ a relationship you can reverse the direction a model estimates for the relationship (Simpson's paradox).

> Do you consider this situation unjust? If so, you might be unhappy to learn that the entire goal of the field of algorithmic fairness is to do something along these lines.

I don't think the creation of tools to accommodate this specific purpose is bad, per se. Whether or not they are the appropriate tool to use is a different question.

Re: Approaching fairness in machine learning

#54

Post by [deleted] »

yummyfajitas wrote: post #40
danso wrote: post #33

Earlier quoted context omitted.

> The issue is a simple factual question of whether, all else held equal, black people repay their loans at the same rate as whites in identical financial circumstances Oh if you put it that way, then I don't know. Because that's not the reality that's being dealt with, in which whites and blacks have identical circumstances. I think you're reading something into this that others aren't.

Because that's not the reality that's being dealt with, in which whites and blacks have identical circumstances. Of course it is. There may be 5 blacks and 100 whites with a credit score of 830. But as long as blacks and whites with an 830 credit score behave the same, then data from whites will generalize to blacks and the problem tlb brought up doesn't apply. Redundant encoding is also irrelevant - this is useless…

[deleted]

Re: Approaching fairness in machine learning

#55

Post by SamReidHughes »

yummyfajitas wrote: post #40
danso wrote: post #33

Earlier quoted context omitted.

> The issue is a simple factual question of whether, all else held equal, black people repay their loans at the same rate as whites in identical financial circumstances Oh if you put it that way, then I don't know. Because that's not the reality that's being dealt with, in which whites and blacks have identical circumstances. I think you're reading something into this that others aren't.

Because that's not the reality that's being dealt with, in which whites and blacks have identical circumstances. Of course it is. There may be 5 blacks and 100 whites with a credit score of 830. But as long as blacks and whites with an 830 credit score behave the same, then data from whites will generalize to blacks and the problem tlb brought up doesn't apply. Redundant encoding is also irrelevant - this is useless…

Having the same credit score, or other data, does not mean they have identical circumstances. For example, your willingness to follow the rules of society, even to your detriment, might be a result of whether society's rules have treated you fairly in the past.

So belief that you'll get different default rates given some financial data does not imply that you are a scientific racist. To create identical circumstances you'd have to do a brain swap (and some other relevant internal organs) on some black and white infants. A scientific racist view is that then the likelihood of paying off the loans would follow the brain, not the skin.

Re: Approaching fairness in machine learning

#56

Post by [deleted] »

closed wrote: post #53
yummyfajitas wrote: post #42

Earlier quoted context omitted.

Yes, if it's actually (blue & Idaho) ~> default, and your model ignores blue, then the greens will pay a higher cost. If color is redundantly encoded then your model can partially fix this and penalize the blue's in Idaho. Do you consider this situation unjust? If so, you might be unhappy to learn that the entire goal of the field of algorithmic fairness is to do something along these lines.

> Available data on blacks specifically is completely irrelevant if blacks and whites aren't fundamentally different. The white model will generalize. I should have been more clear that I was responding to this part of your comment. That even if blacks and whites aren't fundamentally different (in the sense that your race does not directly cause an outcome of interest) you can produce biases that are essentially a mi…

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