On statistical criteria of algorithmic fairness

2021/03/01 by Brian Hedden · 132 citations
Arts and Humanities · Neuroscience · Social Sciences · #Citation #Computer science #Epistemology, Ethics, and Metaphysics #Ethics and Social Impacts of AI #Library science #Psychology of Moral and Emotional Judgment #Sociology

paper · pdf · doi:10.1111/papa.12189

published in Philosophy &amp Public Affairs 49(2), 209-231 (Wiley)

openalex publication_date 2021/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm's predictions and the actual outcomes, for instance requiring that the rate of false positives be equal across the relevant groups.

Citations

• Algorithmic bias: on the implicit biases of social technology

Cited by

• Is there a tension between AI safety and AI welfare?
• The Right to a Justification
• Broomean(ish) Algorithmic Fairness?
• Marginal Fairness: Fair Decision-Making under Risk Measures

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• Algorithmic Fairness and Statistical Discrimination
• Designing Equitable Algorithms
• Detecting Statistically Significant Fairness Violations in Recidivism Forecasting Algorithms
• Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges