Balanced Ranking with Diversity Constraints
This work addresses fairness issues in algorithmic selection for historically disadvantaged populations, but it is incremental as it builds on existing diversity constraints.
The paper tackles the problem of reduced in-group fairness in set selection and ranking algorithms with diversity constraints, by introducing additional constraints to balance fairness across groups and formalizing optimization problems as integer linear programs, resulting in quantified trade-offs between balance and overall performance in experiments with real datasets.
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.