Resolving the Disparate Impact of Uncertainty: Affirmative Action vs. Affirmative Information

نویسندگان

چکیده

Algorithmic risk assessments hold the promise of greatly advancing accurate decision-making, but in practice, multiple real-world examples have been shown to distribute errors disproportionately across demographic groups. In this paper, we characterize why error disparities arise first place. We show that predictive uncertainty often leads classifiers systematically disadvantage groups with lower-mean outcomes, assigning them smaller true and false positive rates than their higher-mean counterparts. This can occur even when prediction is group-blind. prove avoid these imbalances, individuals must either be over-represented among classifications or assigned more predictions those focus on latter condition as a solution bridge rate divides data acquisition for low-mean increase access opportunity. call strategy “affirmative information” compare it traditional affirmative action classification task identifying creditworthy borrowers.

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ژورنال

عنوان ژورنال: Social Science Research Network

سال: 2021

ISSN: ['1556-5068']

DOI: https://doi.org/10.2139/ssrn.3789058