An impact assessment of machine learning risk forecasts on parole board decisions and recidivism

By Richard A. Berk. 2017

129 citations recorded by the source corpus.

Subfields: Safety, Risk, Reliability and Quality, Sociology and Political Science

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Citations in the corpus

Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, background): “Ridgeway and Berk (2017) apply a similar individual approach to stochastic gradient boosting.”
Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations (2018, methodology): “For example, ML is now being used to predict recidivism in the criminal justice system [Berk, 2017], interventions in the child welfare system [Cuccaro-Alamin et al. , 2017], and cancer from radiology images [Esteva et al. , 2017].”
Algorithmic Risk Assessment in the Hands of Humans (2019, unknown): “This small literature includes studies of algorithms in prison placement (Berk et al., 2002), parole (Berk, 2017) and pretrial detention decisions (Stevenson, 2019; Sloan et al., 2018; Albright, 2019; Cowgill, 2019) – and in some non-criminal-justice contexts such as hiring (Cowgill, 2018).”
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks (2019, background): “With the deployment of AI-based systems in high risk fields such as medical diagnosis [24], autonomous vehicle control [20] and the legal sector [1], the major challenges of the upcoming era are thus going to be in issues of uncertainty and trust-worthiness of a classifier.”
The Price of Interpretability (2019, background): “Predictive models are used in an increasingly high-stakes set of applications, from bail decisions in the criminal justice system [2, 23] to treatment recommendations in personalized medicine [4].”
Economics, Fairness and Algorithmic Bias (2020, unknown): “19Cowgill (2018a), Stevenson and Doleac (2018) and Berk (2017) use regression discontinuities in various ways to examine algorithmic fairness questions.”