RISK, RACE, AND RECIDIVISM: PREDICTIVE BIAS AND DISPARATE IMPACT*: RISK, RACE, AND RECIDIVISM

By J. Skeem, Christopher T. Lowenkamp. 2016

211 citations recorded by the source corpus.

Subfields: Clinical Psychology, Sociology and Political Science

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Citations in the corpus

Algorithmic Decision Making and the Cost of Fairness (2017, background): “10Some researchers also check whether the AUC of scores is similar across race groups [38].”
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks (2021, background): “Many RAI bias studies apply these standards [88, 89, 90, 91, 13] to consider instrument performance across the full range of scores rather than binary or categorical classiﬁcations.”
False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks" (2016, unknown): “The framework is reviewed and applied to a risk assessment by Skeem and Lowenkamp (2016a, 2016b) and Skeem, Monahan, and Lowenkamp (2016).”
Can we avoid reductionism in risk reduction? (2017, background): “…2015; Hannah-Moffat et al., 2009; Monahan and Skeem, 2014, 2016; Skeem, 2013; Taxman and Marlowe, 2006; Taxman et al., 2006), and its social and political ramifications (e.g. Hannah-Moffat, 2004; HannahMoffat and Maurutto, 2010; Harcourt, 2015; O’Malley, 2012; Skeem and Lowenkamp, 2016).”
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning (2018, background): “Others have examined whether algorithmic predictions in criminal justice have similar AUC across protected groups, a form of classification parity (Skeem and Lowencamp, 2016), and whether such predictions are calibrated (Skeem et al.”
A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions (2018, background): “In this literature instruments are tested for what is known as differential prediction, otherwise known as predictive bias (Skeem and Lowenkamp, 2016).”
Algorithmic Fairness: Choices, Assumptions, and Definitions (2021, background): “…debate about whether this adequately addresses problems of un-fairness, one proposed solution is to avoid predicting rearrest for any crime and instead predict rearrest for violent crime, as it is argued that the latter outcome variable suffers from less measurement error (Skeem & Lowenkamp 2016).”