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by currymj
2373 days ago
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most of the standard metrics of fairness for machine learning don't just just try to equalize proportions of positive/negative labels. they look at error rates. under these measures of fairness, a perfectly accurate predictor is regarded as perfectly fair, regardless of a disparity in base rates in the two populations. some of the predictive policing models still fail under these metrics -- they are more prone to make errors on black defendants. |
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Unless your predictor is perfectly accurate, the errors will be proportional to the base rate. If you're predicting that more X will do Y then you have more chances to be wrong.
Improving accuracy is the only real way to reduce the error rate. If you can't do that then you're left with malicious nonsense like fudging the base rate, which is just trading false positives for false negatives and not actually making anything better.