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As algorithmic decision-making systems are becoming more pervasive, it iscrucial to ensure such systems do not become mechanisms of unfairdiscrimination on the basis of gender, race, ethnicity, religion, etc.Moreover, due to the inherent trade-off between fairness measures and accuracy,it is desirable to learn fairness-enhanced models without significantlycompromising the accuracy. In this paper, we propose Pareto efficient Fairness(PEF) as a suitable fairness notion for supervised learning, that can ensurethe optimal trade-off between overall loss and other fairness criteria. Theproposed PEF notion is definition-agnostic, meaning that any well-definednotion of fairness can be reduced to the PEF notion. To efficiently find a PEFclassifier, we cast the fairness-enhanced classification as a bileveloptimization problem and propose a gradient-based method that can guarantee thesolution belongs to the Pareto frontier with provable guarantees for convex andnon-convex objectives. We also generalize the proposed algorithmic solution toextract and trace arbitrary solutions from the Pareto frontier for a givenpreference over accuracy and fairness measures. This approach is generic andcan be generalized to any multicriteria optimization problem to trace points onthe Pareto frontier curve, which is interesting by its own right. Weempirically demonstrate the effectiveness of the PEF solution and the extractedPareto frontier on real-world datasets compared to state-of-the-art methods.

Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing