  In machine learning (ML) applications, unfair predictions may discriminate
against a minority group. Most existing approaches for fair machine learning
(FML) treat fairness as a constraint or a penalization term in the optimization
of a ML model, which does not lead to the discovery of the complete landscape
of the trade-offs among learning accuracy and fairness metrics, and does not
integrate fairness in a meaningful way.
  Recently, we have introduced a new paradigm for FML based on Stochastic
Multi-Objective Optimization (SMOO), where accuracy and fairness metrics stand
as conflicting objectives to be optimized simultaneously. The entire trade-offs
range is defined as the Pareto front of the SMOO problem, which can then be
efficiently computed using stochastic-gradient type algorithms. SMOO also
allows defining and computing new meaningful predictors for FML, a novel one
being the Sharpe predictor that we introduce and explore in this paper, and
which gives the highest ratio of accuracy-to-unfairness. Inspired from SMOO in
finance, the Sharpe predictor for FML provides the highest prediction return
(accuracy) per unit of prediction risk (unfairness).
