Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions

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Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure (2019, background): “For example, ML and artificial intelligence (AI) are already being used to control end-to-end autonomous vehicles [2], how long a criminal should spend in prison [6], the order in which a person is presented the news [27], or even diagnose and treat medical patients [24].”
Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, background): “For example, a confusion table for release decisions at arraignments from a given month might be used to draw inferences about a full year of arraignments in that jurisdiction (Berk and Sorenson 2016).”
Proxy Non-Discrimination in Data-Driven Systems (2017, background): “Also, these models represent a significant fraction of models used in practice in predictive systems that operate on personal information, ranging from advertising [13], psychopathy [31], criminal justice [7, 8], and actuarial sciences [27, 29].”
Algorithmic Risk Assessment in the Hands of Humans (2019, background): “2See, e.g., (Chetty et al., 2014; Berk et al., 2016; Goel et al., 2016; Chalfin et al., 2016; Jung et al., 2017; Kleinberg et al., 2018).”