Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases

Jeffrey Grogger (), Sean Gupta (), Ria Ivandic () and Tom Kirchmaier ()
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Jeffrey Grogger: University of Chicago - Harris School of Public Policy; NBER
Sean Gupta: London School of Economics and Political Science - Center for Economic Performance
Ria Ivandic: London School of Economics and Political Science - Center for Economic Performance

No 2021-01, Working Papers from Becker Friedman Institute for Research In Economics

Abstract: We compare predictions from a conventional protocol-based approach to risk assessment with those based on a machine-learning approach. We first show that the conventional predictions are less accurate than, and have similar rates of negative prediction error as, a simple Bayes classifier that makes use only of the base failure rate. Machine learning algorithms based on the underlying risk assessment questionnaire do better under the assumption that negative prediction errors are more costly than positive prediction errors. Machine learning models based on two-year criminal histories do even better. Indeed, adding the protocol-based features to the criminal histories adds little to the predictive adequacy of the model. We suggest using the predictions based on criminal histories to prioritize incoming calls for service, and devising a more sensitive instrument to distinguish true from false positives that result from this initial screening.

JEL-codes: K14 K36 (search for similar items in EconPapers)
Pages: 67 pages
Date: 2021
New Economics Papers: this item is included in nep-big and nep-law
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Comparing Conventional and Machine‐Learning Approaches to Risk Assessment in Domestic Abuse Cases (2021)
Working Paper: Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases (2020)
Working Paper: Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases (2020)
Working Paper: Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:bfi:wpaper:2021-01

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