Comparing Meters to Yards: A Nationally Representative Evaluation of Gender Bias in Risk Assessment

Abstract

Risk-needs assessments (RNAs) assist correctional staff in assigning supervision and programming. While gender is a well-known predictor of crime, for decades contemporary RNAs have claimed “gender-neutrality” or risk prediction equality for males and females. Unfortunately, females are frequently overclassified, relegated to a category higher than their risks indicate. Using ridge and mixed effects regression methods, we sought to enhance the predictive accuracy of the Modified Positive Achievement Change Tool (MPACT) comparing three common assessment development methods via a 10-state sample of youth (N = 241,596) across multiple justice settings. Findings demonstrated recidivism rates vary substantially by gender and bias/overclassification is likely for assessments using a “gender-neutral” approach. Tools that oversample females, or attempt “equally weighting,” have similar issues. However, “gender-specific” methods create prediction parity, rooting out overclassification. Study takeaways include the need to evaluate tools for overclassification, methods of eliminating gender bias, while achieving strong predictive validity in development of the MPACT.

Keywords:

• Juvenile justice
• risk-need assessment
• recidivism
• bias
• prediction

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1 In this study, we limit our discussion to the binary coding of men/women and boys/girls, while acknowledging non-binary categories exist, and will become an important consideration for the current discussion. Unfortunately, current data collection and agency categorization drastically reduce the feasibility of non-binary studies in the justice field.

2 For reviewed studies overclassification is defined as female rates of recidivism presenting as lower than males across risk level categories

3 See Figure 3.

4 We note that, the OYAS developer’s method of upwardly adjusting female High-Risk cut points is similar in approach.