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

Jeffrey Grogger, Sean Gupta, Ria Ivandic and Tom Kirchmaier ()

No 28293, NBER Working Papers from National Bureau of Economic Research, Inc

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)
Date: 2020-12
New Economics Papers: this item is included in nep-big and nep-cmp
Note: CH LE LS
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Published as Jeffrey Grogger & Sean Gupta & Ria Ivandic & Tom Kirchmaier, 2021. "Comparing Conventional and Machine‐Learning Approaches to Risk Assessment in Domestic Abuse Cases," Journal of Empirical Legal Studies, vol 18(1), pages 90-130.

Downloads: (external link)
http://www.nber.org/papers/w28293.pdf (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)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:28293

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w28293

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().