Overview of: “Statistical Procedures for Forecasting Criminal Behavior: A Comparative Assessment”

By Richard A. Berk, Justin Bleich. 2013

80 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “Transparency of how a decision is made is a necessity in this critical domain, yet very few works are made towards making automated decision making in legal system explainable [49]–[51].”
Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, background): “Berk and Bleich (2013) demonstrated the advantage of using RF and the associated PDPs to accurately model predictor-response relationships under asymmetric classification costs that often arise in criminal justice settings. In the ecological literature, Elith, Leathwick, and Hastie (2008), who relie”
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).”
Using Regression Kernels to Forecast A Failure to Appear in Court (2014, methodology): “The experience comparing logistic regression to machine learning is similar (Berk and Bleich, 2013).”
Sacrificing Accuracy for Transparency in Recidivism Risk Assessment: The Impact of Classification Method on Predictive Performance (2016, background): “Finally, Berk and Bleich (2013) reported their machine learning model, based on Random Forests, signi ﬁ cantly outperformed logistic regression.”
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence (2021, background): “…extraction to indicate how important the association of a given input feature (e.g., Nora’s longevity at her workplace or the temperature) is to an output prediction (e.g., of Nora’s duration of stay at the given job or the number of bikers in the streets) [28, 29, 30, 31, 5, 32, 33, 34, 35, 36].”
Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions (2016, background): “For violent street crimes, the ages of highest risk are the late teens and early 20s, after which the risk of perpetration drops dramatically (Berk & Bleich 2013).”