Scoring functions are sub-optimal for all utility-fairness trade-offs in ranking under a generic fairness formulation, but semi-greedy post-processing can approach the performance of exhaustive post-processing.
Fairness in ranking, part i: Score-based ranking.ACM Comput
2 Pith papers cite this work. Polarity classification is still indexing.
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A game-theoretic framework and algorithms are introduced to maximize beneficial information from ML systems while minimizing biased influences arising from conflicts of interest.
citing papers explorer
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Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
Scoring functions are sub-optimal for all utility-fairness trade-offs in ranking under a generic fairness formulation, but semi-greedy post-processing can approach the performance of exhaustive post-processing.
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Learning with Conflicts of Interest
A game-theoretic framework and algorithms are introduced to maximize beneficial information from ML systems while minimizing biased influences arising from conflicts of interest.