Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases

Jeffrey Grogger, Ria Ivandic and Tom Kirchmaier ()

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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. A random forest based on the underlying risk assessment questionnaire does better under the assumption that negative prediction errors are more costly than positive prediction errors. A random forest based on two-year criminal histories does better still. Indeed, adding the protocol-based features to the criminal histories adds almost nothing 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.

Keywords: domestic abuse; risk assessment; machine learning (search for similar items in EconPapers)
JEL-codes: K42 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2020-02-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-gen and nep-law
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://eprints.lse.ac.uk/104159/ Open access version. (application/pdf)

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 (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)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:104159

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