Fairness with censorship and group constraints

Fairness in machine learning (ML) has gained attention within the ML community and the broader society beyond with many fairness definitions and algorithms being proposed. Surprisingly, there is little work quantifying and guaranteeing fairness in the presence of uncertainty which is prevalent in many socially sensitive applications, ranging from marketing analytics to actuarial analysis and recidivism prediction instruments. To this end, we revisit fairness and reveal idiosyncrasies of existing fairness literature assuming certainty on the class label that limits their real-world utility. Our primary contributions are formulating fairness under uncertainty and group constraints along with a suite of corresponding new fairness definitions and algorithm. We argue that this formulation has a broader applicability to practical scenarios concerning fairness. We also show how the newly devised fairness notions involving censored information and the general framework for fair predictions in the presence of censorship allow us to measure and mitigate discrimination under uncertainty that bridges the gap with real-world applications. Empirical evaluations on real-world datasets with censorship and sensitive attributes demonstrate the practicality of our approach.