[joe_the_user]

Your posts misunderstand both the article and the idea of fairness.

Also from the abstract: "In some cases, black defendants are substantially more likely than white defendants to be incorrectly classified as high risk."

The point is that an AI is a black box that may not explicitly use race but since it's using a variety of criteria rather opaquely, it may effectively, indirectly, use race, the neighborhood someone lives in, their social status or all sorts of things that aren't fair based on "your personal circumstances should determine whether you are considered a risk".

The authors propose a system to mitigate the problems here, though I actually don't really think that's the solution - these AIs should simply be abolished and replaced by objective criteria.

[Houshalter]

>Also from the abstract: "In some cases, black defendants are substantially more likely than white defendants to be incorrectly classified as high risk."

But that's not really true: https://www.chrisstucchio.com/blog/2016/propublica_is_lying....

>these AIs should simply be abolished and replaced by objective criteria.

What exactly is "objective criteria"? Previously human judges made these decisions. They are far more biased than any statistical algorithm.

[avinium]

> Also from the abstract: "In some cases, black defendants are substantially more likely than white defendants to be incorrectly classified as high risk."

I did see that in the abstract, but then the only follow-up in the actual body of the paper was:

"...among defendants who ultimately did not reoffend, blacks were more than twice as likely as whites to be labeled as risky."

Which is simply restating the premise in a roundabout fashion - i.e. that those with criminal records (etc) are more likely to reoffend, and black defendants are more likely to have criminal records. It's a bit of a long bow to draw to say the classification was "incorrect" post-hoc because "those from the risky group who did not reoffend were initially labelled as high risk".

> The point is that an AI is a black box that may not explicitly use race but since it's using a variety of criteria rather opaquely, it may effectively, indirectly, use race, the neighborhood someone lives in, their social status or all sorts of things that aren't fair based on "your personal circumstances should determine whether you are considered a risk".

I believe it's a straightforward logistic regression, which is about as objective as you can be. It's not "AI", it's not opaque and it's not indirect. It's a simple calculation of the relative odds of recidivism based on factors such as criminal history, employment, and so on.

Given that adjusting for race makes no difference to the model output, it is literally the most colour-blind approach you could take.

The authors, on the other hand, would have you treat two people differently purely on the basis of respective race.

Now obviously there are historical/legacy social issues at play, not to mention the broader question of what's "best" to eliminate disadvantage. But I suspect not many people will agree with the authors' that this would be "fair".

If I were pushing their line of thinking, I would be researching whether this type of "positive" discrimination actually reduces the race gap long-term.

That's ultimately the only thing that matters, and in my mind, it's a much easier sell to say "this isn't fair, but it's what we need to do to reduce the criminal overrepresentation and disadvantage that exist because of our history with slavery".

[joe_the_user]

I did see that in the abstract, but then the only follow-up in the actual body of the paper was:

Meaning your original comment was disingenuous. Good job.

[AstralStorm]

How do you figure out if criteria are unbiased without actually implementing an unbiased estimator though? (Not an AI. A statistical Bayesian-like where we can reason on inputs and outputs.)