PreFAIR: Combining Partial Preferences for Fair Consensus Decision-making.

Preference aggregation mechanisms help decision-makers combine diverse preference rankings produced by multiple voters into a single consensus ranking. Prior work has developed methods for aggregating multiple rankings into a fair consensus over the same set of candidates. Yet few real-world problems present themselves as such precisely formulated aggregation tasks with each voter fully ranking all candidates. Instead, preferences are often expressed as rankings over partial and even disjoint subsets of candidates. For instance, hiring committee members typically opt to rank their top choices instead of exhaustively ordering every single job applicant. However, the existing literature does not offer a framework for characterizing nor ensuring group fairness in such partial preference aggregation tasks. Unlike fully ranked settings, partial preferences imply both a selection decision of whom to rank plus an ordering decision of how to rank the selected candidates. Our work fills this gap by conceptualizing the open problem of fair partial preference aggregation. We introduce an impossibility result for fair selection from partial preferences and design a computational framework showing how we can navigate this obstacle. Inspired by Single Transferable Voting, our proposed solution PreFair produces consensus rankings that are fair in the selection of candidates and also in their relative ordering. Our experimental study demonstrates that PreFair achieves the best performance in this dual fairness objective compared to state-of-the-art alternatives adapted to this new problem while still satisfying voter preferences.