The Role of Race in Forecasts of Violent Crime

By Richard A. Berk. 2009

50 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistics and Probability, AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, background): “One approach is to remove all linear dependence between L and S (Berk 2008).”
Algorithmic Decision Making and the Cost of Fairness (2017, background): “If the vector of features x includes race, an individual’s risk score p | x is in theory statistically robust to this issue, and for this reason some have argued race should be included in risk models [7].”
Interpretable classification models for recidivism prediction (2015, background): “Some studies have shown that race is important for accurate recidivism prediction (Petersilia and Turner 1987, Berk 2009).”
Forecasts of Violence to Inform Sentencing Decisions (2013, background): “One might ordinarily apply a tree-based form of machine learning already shown to be effective in related circumstances (Barnes et al. 2010; Berk et al. 2005, 2006, 2009).”
Asymmetric Loss Functions for Forecasting in Criminal Justice Settings (2011, background): “In many instances, relative costs can be elicited from stakeholders, at least to the satisfaction of those stakeholders (Berk et al. 2009; Berk 2009 ).”
Decoupled classifiers for fair and efficient machine learning (2017, methodology): “As algorithms are increasingly used to make decisions of social consequence, the social values encoded in these decision-making procedures are the subject of increasing study, with fairness being a chief concern (Pedreshi et al., 2008; Zliobaite et al., 2011; Kamishima et al., 2011; Dwork et al., 20”
Balancing the Costs of Forecasting Errors in Parole Decisions (2010, methodology): “Random forests have been effective in other related work (Berk et al. 2009, Berk, 2009).”
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees (2018, background): “, against black people while predicting future criminals [27, 5, 7], granting loans [19] or NYPD stop-and-frisk [30], and against women while recommending jobs [18].”
RISK, RACE, AND RECIDIVISM: PREDICTIVE BIAS AND DISPARATE IMPACT*: RISK, RACE, AND RECIDIVISM (2016, background): “This is unlikely, given that criminal history typically predicts recidivism much more strongly than does race (Berk, 2009; Durose, Cooper, and Snyder, 2014).”
What You Can and Can’t Properly Do with Regression (2010, background): “It is then a small step to build visualizations that take both time and space into account (Berk and MacDonald 2009).”
Decoupled Classifiers for Group-Fair and Efficient Machine Learning (2017, methodology): “As algorithms are increasingly used to make decisions of social consequence, the social values encoded in these decision-making procedures are the subject of increasing study, with fairness being a chief concern (Pedreschi et al., 2008; Zliobaite et al., 2011; Kamishima et al., 2011; Dwork et al., 2”
Risk Assessment in Criminal Sentencing. (2016, background): “Because criminal history predicts recidivism more strongly than race, it will dominate or codominate race (Berk 2009; Bonta, Law, & Hanson 1998; Durose, Cooper & Snyder 2014).”