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Research map / Learning theory and algorithms / Fairness

Fairness in Matching under Uncertainty

ICML 2023 · Learning theory and algorithms › Fairness · approach: Causal and policy fairness in decisions · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the LP-based fair allocation instead of the mix of Thompson sampling and a utility-maximizing matchingLibimseti matching experiment · Libimsetiexpected welfareHelped
Use the LP-based solution rather than Thompson sampling at full fairnessLibimseti matching experiment · Libimseti, φ=1expected welfareHelped
Set φ=0, leaving the principal unconstrained by fairnessLP-based allocation and A_mix baseline · Libimseti, φ=0expected welfareNo clear effect
Substitute sampled estimates directly for the true fairness requirements in the LP, without the correction (ablation)Fair utility-maximizing matching LP · Matching under uncertaintyfairnessHurt
Substitute sampled estimates directly for the true fairness requirements in the LP, without the correction (ablation)Fair utility-maximizing matching LP · Matching under uncertaintyprincipal utilityHurt
Add the correction widehat{ell}_{x,k}=(tilde{ell}_{x,k}+k epsilon)/(n epsilon+1) to sampled fairness requirementsFair utility-maximizing matching LP · Matching under uncertaintyutility approximation: 1/(φ n ε + 1)Helped
Add the correction widehat{ell}_{x,k}=(tilde{ell}_{x,k}+k epsilon)/(n epsilon+1) to sampled fairness requirementsFair utility-maximizing matching LP · Matching under uncertaintyfairness: φ · (1 + ε/2)/(n ε+1)Helped
Use the modified LP with the tightness construction's perturbed fairness inputs (ablation)Sampling-based fair allocation · Fair ranking instance, φ=1utility approximation: 1/(n ε + 1)Hurt
Use the modified LP with the tightness construction's perturbed fairness inputs (ablation)Sampling-based fair allocation · Fair ranking instance, φ=1fairness: (1+ε/2)/(nε+1)Hurt

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