Statistical Procedures for Forecasting Criminal Behavior

By Richard A. Berk, Justin Bleich. 2013

162 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Sociology and Political Science, Statistics and Probability, AI Ethics, Fairness & Society

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Finally, CERTI-FAI (Counterfactual Explanations for Robustness, Transparency, Inter-pretability, and Fairness of Artiﬁcial Intelligence models) [402] uses a customized genetic algorithm to generate counterfactuals that can help to see the robustness of a ML model, generate explanations, and examine ”
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.”
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).”
Interpretable classification models for recidivism prediction (2015, methodology): “This discussion is captured nicely by Bushway (2013), who described a discrepancy between papers of Berk and Bleich (2013) and Tollenaar and van der Heijden (2013).”
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).”
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).”
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.”
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data (2018, background): “Robustness to adversarial perturbation has become an extremely important criterion for applications of machine learning in security-sensitive domains such as spam detection [25], fraud detection [6], criminal justice [3], malware detection [13], and ﬁnancial markets [27].”