Abstract
Many set selection and ranking algorithms have recently been enhanced with diversity constraintsthat
aim to explicitly increase representation of historically disadvantaged populations, or to improve the
overall representativeness of the selected set. An
unintended consequence of these constraints, however, is reduced in-group fairness: the selected candidates from a given group may not be the best
ones, and this unfairness may not be well-balanced
across groups. In this paper we study this phenomenon using datasets that comprise multiple sensitive attributes. We then introduce additional constraints, aimed at balancing the in-group fairness
across groups, and formalize the induced optimization problems as integer linear programs. Using
these programs, we conduct an experimental evaluation with real datasets, and quantify the feasible trade-offs between balance and overall performance in the presence of diversity constraints