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We consider the problem of subset selection where one is given multiplerankings of items and the goal is to select the highest ``quality'' subset.Score functions from the multiwinner voting literature have been used toaggregate rankings into quality scores for subsets. We study this setting ofsubset selection problems when, in addition, rankings may contain systemic orunconscious biases toward a group of items. For a general model of inputrankings and biases, we show that requiring the selected subset to satisfygroup fairness constraints can improve the quality of the selection withrespect to unbiased rankings. Importantly, we show that for fairnessconstraints to be effective, different multiwinner score functions may requirea drastically different number of rankings: While for some functions, fairnessconstraints need an exponential number of rankings to recover aclose-to-optimal solution, for others, this dependency is only polynomial. Thisresult relies on a novel notion of ``smoothness'' of submodular functions inthis setting that quantifies how well a function can ``correctly'' assess thequality of items in the presence of bias. The results in this paper can be usedto guide the choice of multiwinner score functions for the subset selectionsetting considered here; we additionally provide a tool to empirically enablethis.

Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions