Beyond Traditional Risk Scores: Tackling LS/CMI Offender Misclassifications with Machine Learning
ObjectivesThis paper investigates the accuracy of offender risk assessment scoring methods. We study the degree of misclassification resulting from the conventional practice of aggregating individual items to derive risk scores and categories. We document which types of offenders are prone to miscla...
| Authors: | ; ; ; ; |
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| Format: | Electronic Article |
| Language: | English |
| Published: |
2025
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| In: |
Journal of quantitative criminology
Year: 2025, Volume: 41, Issue: 4, Pages: 549-574 |
| Online Access: |
Volltext (lizenzpflichtig) |
| Check availability: | HBZ Gateway |
| Keywords: |
| Summary: | ObjectivesThis paper investigates the accuracy of offender risk assessment scoring methods. We study the degree of misclassification resulting from the conventional practice of aggregating individual items to derive risk scores and categories. We document which types of offenders are prone to misclassification, particularly in relation to age and gender.MethodsWe use a machine learning algorithm to leverage the rich set of information available in the LS/CMI. Using all 45,535 assessments conducted between 2008 and 2015 in Quebec (Canada), we estimate probabilities from a random forest algorithm to predict individual risks of recidivism over a two-year follow-up. We compare the resulting probabilities to those inferred from the risk scores or categories to document the extent of misclassification. We devise a simple algorithm to construct alternative risk categories that reduce misclassification relative to the LS/CMI total scores and categories.Results The probabilities obtained from the random forest approach accurately predict individual probabilities to reoffend. Compared with these predictions, the traditional aggregation of items into risk scores or categories yields substantial misclassification for certain groups of offenders. In particular, we find that the risk associated with older individuals when using the LS/CMI risk categories is overestimated by about 10 percentage points. Our alternative risk categories, devised from our machine learning predictions, successfully avoid such misclassification.Conclusions Traditional methods of aggregating items from risk assessments into scores may lead to substantial misclassification, especially for older offenders. Misclassification arises from 1) items not being equally risk-relevant; 2) information collected by the LS/CMI being excluded or overly simplified when constructing scores; and 3) age being omitted from risk scores. Machine learning algorithms avoid these pitfalls and can be used to construct less biased categories. |
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| ISSN: | 1573-7799 |
| DOI: | 10.1007/s10940-025-09606-w |
