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by umvi
1768 days ago
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How do you tell if something is biased or not? Seems like the current system is "if people cry foul because it seems unfair, then the model is biased" which doesn't seem scientifically rigorous. This seems like a hard problem. For example, say that you have an ML model that decides whether someone will be a good sports athlete or not purely based on biometrics (blood oxygen level, blood pressure, BMI, reflex time, etc.). If the model starts predicting black people will be better athletes at higher rates than white people, is the ML model biased? Or is the reality that black people have higher-than-average advantageous physical characteristics? How do you tell the difference between bias and reality? |
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My comment is the naughtiest of wrong-think by HN standards, but the likely reality is that most human programmers will do their genuine best to deliver algorithms that treat all humans equally without bias towards race, gender, and other immutable characteristics and just try and deliver the best objective result (picking good athletes, getting the most money, getting the best employees, or whatever other task is involved), but then will be forced to either do 1 of 2 things when it inevitably yields an unequal outcome or decision that goes against the political correctness orthodoxy.
1) Reprogram their software to fit modern political correctness standards. Personally I think this is close to impossible. As an example: say you're creating some software to determine healthiness by various available data and it objectively determines that heavier people tend to be less healthy. You're boxed into an impossible corner here of either being politically incorrect or just lying to people about their health.
2) Go back to human decision makers for anything controversial because I don't even know how it will be possible to program an algorithm to take into account all of society's made-up, arbitrary, ever-changing rules on "equitable" outcomes. As far as I'm aware, Amazon had to abandon their effort to replace some of their HR efforts with algorithms because it yielded politically incorrect outcomes despite the programmers seemingly trying to just come up with the best possible employees and nothing else.