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Minimax AUC Fairness: Efficient Algorithm with Provable Convergence

AAAI 2023 · Learning theory and algorithms › Fairness · approach: Post-processing and transport for fairness · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Optimize the minimum across all intra- and inter-group AUCs instead of unconstrained overall AUCAUCMax training on Adult · AdultMin/Max group AUC: .823±.005 -> .953±.002Helped
Optimize the minimum across all intra- and inter-group AUCs instead of unconstrained overall AUCAUCMax training on Bank · BankMin/Max group AUC: .780±.018 -> .858±.014Helped
Optimize the minimum across all intra- and inter-group AUCs instead of unconstrained overall AUCAUCMax training on Compas · CompasOverall AUC: .732±.004 -> .741±.004Helped
Optimize the minimum across all intra- and inter-group AUCs instead of unconstrained overall AUCAUCMax training on Default · DefaultOverall AUC: .763±.005 -> .767±.002Helped
Optimize the minimum across all intra- and inter-group AUCs instead of unconstrained overall AUCAUCMax training on Adult · AdultOverall AUC: .902±.002 -> .901±.004Hurt
Optimize the minimum across all intra- and inter-group AUCs instead of unconstrained overall AUCAUCMax training on Bank · BankOverall AUC: .910±.002 -> .907±.004Hurt
Use the minimax AUC objective rather than EqualAUC constraintsAdult · AdultOverall AUC: .886±.003 -> .901±.004Helped
Use the minimax AUC objective rather than EqualAUC constraintsBank · BankOverall AUC: .866±.005 -> .907±.004Helped
Use the minimax AUC objective rather than EqualAUC constraintsBank · BankMin/Max group AUC: .891±.025 -> .858±.014Hurt
Use the minimax AUC objective rather than EqualAUC constraintsCompas · CompasOverall AUC: .731±.003 -> .741±.004Helped
Use the minimax AUC objective rather than EqualAUC constraintsCompas · CompasMin/Max group AUC: .956±.012 -> .961±.012Helped
Use only intra-group AUCs, dropping inter-group AUCs (ablation)MinimaxFairAUC on Adult · AdultIntra-group AUC fairnessMixed

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