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It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Various notions of fairness have been defined, though choosing an appropriate metric is cumbersome. Trade-offs and impossibility theorems make such selection even more complicated and controversial. In practice, users (perhaps regular data scientists) should understand each of the measures and (if possible)arXiv:2109.05697v1 fatcat:xtphjt65jvgnhojpvfq4lqfcd4