What if it turns out there is reason people discriminate using those features (for example that people of race X tend to fail repaying their loans), and the classifier just learned that reason from the data. Why should we be expected to "fix" the reality for the classifier?
It's quite easy to remove "race" as a feature in the program. The more realistic and difficult question is what about features that are correlated with race, but are not race itself, like: zip code, or proportion of spending used on clothing.
Because we're talking about ethics here, not just optimization. Ethics involves finding out what's wrong with reality and working to fix it, no matter how hard it may seem.
We need AI that doesn't simply accept reality "as is", but can also help us fix it. Otherwise AI will be little more than hyper-optimized reflections of our own failures and prejudices. We've had plenty of that already, we don't really need more of it.
"The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man." - George Bernard Shaw
I agree with you that it is generally wrong, i.e. that other factors should come first. But what if the data suggests it? For example if is known that if you lend to someone from race X, there is 80% chance he would not pay back. Would you not incorporate that factor into your decision?
Why should correlations with ethnicity be dismissed?
Epidemiological studies tell us some diseases disproportionally affect certain groups. Should doctors dismiss this knowledge as racist and refrain from screening patients in the affected groups because they might come off as racist?
Yes. We know, more specifically, that genes are to blame for diseases like hemochromatosis [0], sickle cell anemia [1], or Tay-Sachs [2]. We also know, from pedigree collapse [3], that humans broadly form one single race.
Therefore we know that correlations with any definition of ethnicity or race are spurious, because those definitions must be socially constructed, because the gene pool simply does not have the shape that race realists claim that it does.
Think in terms of contraposition. Sure, if race were real, then maybe it might make sense to talk about racial demographics. However, since race clearly is not real, any demographic correlations must be bogus. There is a much simpler explanation for why some skin colors seem socioeconomically advantaged: Because our society itself has bigoted opinions about skin colors, and has practices like redlining [4] which systematically oppress folks.
Because at some point you have to be human about it and realize it’s not fair to evaluate and treat people based on things they can’t control, regardless of statistics.
It should be pretty easy for anyone out there working on such algorithms to remove features that are race-related, or even not collecting such features in the first place. Treat people as individuals, not as representatives of some kind of meaningless tribe.
> Treat people as individuals, not as representatives of some kind of meaningless tribe
I dont't think that's how we make our day to day decisions. I usually treat people as individuals because I expect to be treated the same, but it doesn't make the "tribe" features meaningless. For example I would not live in given neighborhood if its tribe is knwon for [something bad] (this also applies to my own tribe). If I have an object of value, the tribe of a person would certainly be a factor to decide whether to entrust him with it or not. I'm sure a lot of people would do the same. It's just how probabilities are tranlated by the brain.
Because that’s the political trend in the Western world: if the reality does not fit ideology, then try hard to bend it by being vocal and bulling people that don’t agree, and get power in institutions to push them to the mass. Basically the same reasoning USSR used: if communism is failing that’s because we need more communism.
The good point is reality always comme back. It takes decades for the USSR to crumble, but it finally collapsed. In the same way, organization that twisted reality will pay it back one day, for example banks that grant more loans to people that statistically default more will lose more money, thus creating opportunities for companies that has a reality-based strategy to outcompete them.
Another example: let’s imagine a TLA is building a system to mine potential mass shooters. The AI output is 99% male, so the ethics comity step in and imposes 50% of women profiles in output. The next shooting happens. It would have been predicted by the initial system, yet the modified one didn’t because the agency was too busy investing really low probability cases. The problem seems obvious in this case, but when a few selected group keywords are thrown in most people will put aside any logic and approve anything (either by conviction or for virtue signaling).
