Balanced Ranking with Diversity Constraints

Balanced Ranking with Diversity Constraints

Ke Yang, Vasilis Gkatzelis, Julia Stoyanovich

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
AI for Improving Human Well-being. Pages 6035-6042. https://doi.org/10.24963/ijcai.2019/836

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 over-all 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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Special Track on AI for Improving Human-Well Being: AI ethics (Special Track on AI and Human Wellbeing)