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One problem you will run into here is that political use of the word "bias" isn't any sort of statistical claim; it's often just used to mean that something vio
by wyager 10y ago
One problem you will run into here is that political use of the word "bias" isn't any sort of statistical claim; it's often just used to mean that something violates vague social expectations about what information is "acceptable" to use when making decisions. ML algorithms don't care; they will make the best possible decision based on the information they have, even if their though process (so to speak) is "biased" in the political sense that it may use gender, race, nationality, etc. to help make optimal decisions.
- yummyfajitas 10y agoYes, unfortunately the term is overloaded. "Bias" can also mean "making correct decisions that I wish were incorrect". That's why I explicitly defined "bias". In the past era, e.g. 1980-2010, it was possible to use vague emotive language to support all kinds of disparate things. As a concrete example that I touch on in my post, and since racism is the loaded undercurrent of this example, we like to pretend that eliminating racial or sexual bias (in the sense of making wrong decisions) will get us proportional representation. Algorithms are bringing us to an age where analytic philosophy is becoming really important. You can tell an algorithm to give you proportional representation, or you can tell it to be racially/sexually unbiased. But the algorithm will reflect reality and reality may not agree with your assumptions; you can't assume that asking for one will give you the other. So now we get into trolley problems: how much meritocracy/equal opportunity will you sacrifice to get proportional representation? Unlike before, this is now a choice you need to explicitly and openly state and acknowledge.