AI/ML

Balanced Ranking with Diversity Constraints. (arXiv:1906.01747v1 [cs.AI])

Many set selection and ranking algorithms have recently been enhanced with
diversity constraints that 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.

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