  In many real life situations, including job and loan applications,
gatekeepers must make justified and fair real-time decisions about a person's
fitness for a particular opportunity. In this paper, we aim to accomplish
approximate group fairness in an online stochastic decision-making process,
where the fairness metric we consider is equalized odds. Our work follows from
the classical learning-from-experts scheme, assuming a finite set of
classifiers (human experts, rules, options, etc) that cannot be modified. We
run separate instances of the algorithm for each label class as well as
sensitive groups, where the probability of choosing each instance is optimized
for both fairness and regret. Our theoretical results show that approximately
equalized odds can be achieved without sacrificing much regret. We also
demonstrate the performance of the algorithm on real data sets commonly used by
the fairness community.
