On statistical criteria of algorithmic fairness

2021/03/01 by Brian Hedden · 3 citations
Social Sciences · Arts and Humanities · Neuroscience · #Ethics and Social Impacts of AI #Epistemology, Ethics, and Metaphysics #Psychology of Moral and Emotional Judgment

paper · pdf · doi:10.1111/papa.12189

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?

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