  Fairness-aware machine learning for multiple protected at-tributes (referred
to as multi-fairness hereafter) is receiving increasing attention as
traditional single-protected attribute approaches cannot en-sure fairness
w.r.t. other protected attributes. Existing methods, how-ever, still ignore the
fact that datasets in this domain are often imbalanced, leading to unfair
decisions towards the minority class. Thus, solutions are needed that achieve
multi-fairness,accurate predictive performance in overall, and balanced
performance across the different classes.To this end, we introduce a new
fairness notion,Multi-Max Mistreatment(MMM), which measures unfairness while
considering both (multi-attribute) protected group and class membership of
instances. To learn an MMM-fair classifier, we propose a multi-objective
problem formulation. We solve the problem using a boosting approach that
in-training,incorporates multi-fairness treatment in the distribution update
and post-training, finds multiple Pareto-optimal solutions; then uses
pseudo-weight based decision making to select optimal solution(s) among
accurate, balanced, and multi-attribute fair solutions
