The Case Against Categorical Risk Estimates

Forthcoming in Behavioral Sciences and the Law

UC Irvine School of Law Research Paper No. 2018-32

27 Pages Posted: 28 Dec 2017 Last revised: 30 Apr 2018

Date Written: December 22, 2017

Abstract

Risk estimates can be communicated in a variety of forms, including numeric and categorical formats. An example of the latter is “low/medium/high risk.” The categorical format is preferred by judges and practitioners alike, and is mandated by the most commonly utilized forensic risk assessment instruments (the HCR-20 and the Static-99). This article argues against the practice of communicating risk in categorical terms on empirical and normative grounds. Empirically, there is no consensus about what level of risk corresponds to a particular category, such as “high risk.” Moreover, recent studies indicate that categorizing an otherwise continuous risk estimate does not add incremental predictive validity to the risk estimate. Normatively, categorization obscures what is fundamentally a value judgment about the relative costs and benefits of correct (e.g., true positive) and incorrect (e.g., false positive) outcomes. Such a judgment is inherently non-scientific and invades the province of the jury. Indeed, categorical risk estimates are in principle no different than “dangerousness predictions,” which are simply binary and which have been denounced by the field. The fact that alternative risk communication formats have limitations does not justify continuing the pervasive practice of communicating categorical risk estimates.

Keywords: Actuarial risk assessment; risk communication; decision making; numeracy

Suggested Citation

Scurich, Nicholas, The Case Against Categorical Risk Estimates (December 22, 2017). Forthcoming in Behavioral Sciences and the Law; UC Irvine School of Law Research Paper No. 2018-32. Available at SSRN: https://ssrn.com/abstract=3092393

Nicholas Scurich (Contact Author)

University of California, Irvine ( email )

Campus Drive
Irvine, CA 62697-3125
United States

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