4.6 Review

A group of industry, academic, and government experts convene in Philadelphia to explore the roots of algorithmic bias

Related references

Note: Only part of the references are listed.
Article Social Sciences, Mathematical Methods

Fairness in Criminal Justice Risk Assessments: The State of the Art

Richard Berk et al.

Summary: This article clarifies the trade-offs between accuracy and fairness in criminal justice risk assessments, highlighting at least six kinds of fairness which may be incompatible with each other and with accuracy. The differences in base rates across legally protected groups present a major complication in practice, requiring consideration of challenging trade-offs.

SOCIOLOGICAL METHODS & RESEARCH (2021)

Article Statistics & Probability

AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION

James E. Johndrow et al.

ANNALS OF APPLIED STATISTICS (2019)

Article Multidisciplinary Sciences

Semantics derived automatically from language corpora contain human-like biases

Aylin Caliskan et al.

SCIENCE (2017)

Article Computer Science, Interdisciplinary Applications

Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments

Alexandra Chouldechova

BIG DATA (2017)

Article Computer Science, Artificial Intelligence

Three naive Bayes approaches for discrimination-free classification

Toon Calders et al.

DATA MINING AND KNOWLEDGE DISCOVERY (2010)