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A good example of this that I saw recently was AI used for underwriting health insurance. In order to make the algorithm "unbiased" in terms of race they remove
by lkschubert8 7y ago
A good example of this that I saw recently was AI used for underwriting health insurance. In order to make the algorithm "unbiased" in terms of race they removed race from the input dimensions. It then started heavily biasing along "frequency of doctor visits" which turns out to have a strong relationship with race. My takeaway is if equality of outcome is part of the goal that has to be part of your training as opposed to attempting to ignore the input, which seems obvious in hindsight.
- slowmovintarget 7y agoIf you're looking for equality of outcome, why go to the trouble of training a system in the first place? A pre-selected distribution applied arbitrarily is just as good. Frequency of doctor visits seems a good indicator for determining how likely an insurance company will have to pay on a policy. If you want equal outcome, you end up setting a higher premium for everyone, and skip the underwriting altogether. The next part of the discussion would be subsidizing the higher premium for those that can't afford it. Just keep in mind that because there are fingers on the scale, fewer people can afford the "equalized" insurance than could afford the honestly evaluated coverage. (Prices are higher for everyone, so the pool of subscribers ends up being smaller if subscribers can opt out.) It becomes a never-ending cycle of controls and impositions leading to collapse. Have the realistic underwriting, lower prices, and subsidize those (overall) lower premiums when needed.