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Assuming data about the predictor, target, and\nmembership in the protected group are available, we show how to optimally\nadjust any learned predictor so as to remove discrimination according to our\ndefinition. Our framework also improves incentives by shifting the cost of poor\nclassification from disadvantaged groups to the decision maker, who can respond\nby improving the classification accuracy.\n  In line with other studies, our notion is oblivious: it depends only on the\njoint statistics of the predictor, the target and the protected attribute, but\nnot on interpretation of individualfeatures. 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