The Emergence of Machine Learning Techniques in Criminology

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Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, neutral): “…been ongoing discussions about how much better in practice risk assessments derived from machine learning perform compared to risk assessments derived from older, conventional methods (Berk 2012; Berk and Bleich 2013; Brennan and Oliver 2013; Liu et al. 2011; Rhodes 2013; Ridgeway 2013a, 2013b).”
Definitions, methods, and applications in interpretable machine learning (2019, background): “and Graphical Statistics 25(2):464–492.”
Hierarchical interpretations for neural network predictions (2018, background): “…in fields such as medicine (e.g. medical image classification (Litjens et al., 2017)), policy-making (e.g. classification aiding public policy makers (Brennan & Oliver, 2013)), and science (e.g. interpreting the contribution of a stimulus to a biological measurement (Angermueller et al., 2016)).”