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Using algorithms to address trade-offs inherent in predicting recidivism.

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Algorithmic risk assessment in criminal justice involves inherent fairness trade-offs. Debiasing strategies can minimize racial disparities in predicting violent reoffending, with race-inclusive algorithms showing improved calibration.

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Area of Science:

  • Criminal Justice
  • Algorithmic Fairness
  • Risk Assessment

Background:

  • Risk assessment tools are increasingly used in criminal justice reform.
  • Concerns exist regarding algorithmic bias and its impact on racial justice.
  • Fairness trade-offs are unavoidable in recidivism prediction, regardless of method.

Purpose of the Study:

  • To compare the effects of three "debiasing" strategies on algorithmic fairness.
  • To analyze trade-offs between predictive accuracy and racial equity in risk assessment.
  • To evaluate algorithmic performance using violent reoffending as a criterion.

Main Methods:

  • Analysis of a matched sample of 67,784 Black and White federal supervisees.
  • Utilized the Post Conviction Risk Assessment tool.
  • Compared three algorithmic debiasing strategies, including race-inclusive and race-blind approaches.

Main Results:

  • Algorithms strongly predicted violent reoffending (AUC = 0.71-0.72).
  • Varying associations with race were observed (r = 0.00-0.21) across strategies.
  • Providing algorithms with race data maximized calibration and minimized imbalanced error rates.

Conclusions:

  • Algorithmic fairness involves inherent trade-offs between efficiency and equity.
  • Race-inclusive algorithmic strategies can improve calibration and reduce error disparities.
  • Findings have implications for policymakers balancing efficiency and equity concerns.