A Commentary on Statistical Assessment of Violence Recidivism Risk
By Peter B. Imrey, Philip Dawid. 2015
51 citations recorded by the source corpus.
Increasing integration and availability of data on large groups of persons has been accompanied by proliferation of statistical and other algorithmic prediction tools in banking, insurance, marketing, medicine, and other fields (see, e.g., Steyerberg 2009a, b). Controversy may ensue when such tools are introduced to fields traditionally reliant on individual clinical evaluations. Such controversy has arisen about “actuarial” assessments of violence recidivism risk, that is, the probability that someone found to have committed a violent act will commit another during a specified period. Recently, Hart, Michie, and Cooke (2007a) and subsequent papers from these authors in several reputable journals have claimed to demonstrate that statistical assessments of such risks are inherently too imprecise to be useful, using arguments that would seem to apply to statistical risk prediction quite broadly. This commentary examines these arguments from a technical statistical perspective, and finds them seriously mistaken in many particulars. They should play no role in reasoned discussions of violence recidivism risk assessment.
Subfields: Clinical Psychology, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/f11a16a726446df8d6e1b03612751266fbd99e12
Citations in the corpus
- False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks" (2016, unknown): “However, as Imrey and Dawid (2015:18) 3 note, the debates and “… considerations [of using ARAIs in such settings] are properly functions of social policy, not statistical inference.”
- False Positives , False Negatives , and False Analyses : A Rejoinder to “ Machine Bias : There ’ s Software Used Across the Country to Predict Future Criminals . And It ’ s Biased Against Blacks . ” (2017, unknown): “The main ProPublica article is cited as Angwin, Larson, Mattu, and Kirchner (2016) or Angwin et al.”