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Algorithmic risk assessment tools are informed by scientific research concerning which factors are predictive of recidivism and thus support the evidence‐based practice movement in criminal justice. Automated assessments of individualized risk (low, medium, high) permit officials to make more effective management decisions. Computer‐generated algorithms appear to be objective and neutral. But are these algorithms actually fair? The focus herein is on gender equity. Studies confirm that women typically have far lower recidivism rates than men. This differential raises the question of how well algorithmic outcomes fare in terms of predictive parity by gender. This essay reports original research using a large dataset of offenders who were scored on the popular risk assessment tool COMPAS. Findings indicate that COMPAS performs reasonably well at discriminating between recidivists and non‐recidivists for men and women. Nonetheless, COMPAS algorithmic outcomes systemically overclassify women in higher risk groupings. Multiple measures of algorithmic equity and predictive accuracy are provided to support the conclusion that this algorithm is sexist.

Criminal justice stakeholders are strongly concerned with disparities in penalty outcomes. Disparities are problematic when they represent unfounded differences in sentences, an abuse of discretion, and/or potential discrimination based on sociodemographic characteristics. The Article presents an original empirical study that explores disparities in sentences at two levels: the individual case level and the regional level. More specifically, the study investigates upward departures in the United States’ federal sentencing system, which constitutes a guidelines-based structure. Upward departures carry unique consequences to individuals and their effects on the system as they lead to lengthier sentences, symbolically represent a dispute with the guidelines advice, and contribute to mass incarceration. Upward departures are discretionary to district courts and thus may lead to disparities in sentencing in which otherwise seemingly like offenders receive dissimilar sentences, in part because of the tendency of their assigned judges to depart upward (or not). The study utilizes a multilevel mixed model to test the effects of a host of explanatory factors on the issuance of upward departures at the case level and whether those same factors are significant at the group level-i.e., district courts-to determine the extent of variation across districts. The explanatory variables tested include legal factors (e.g., final offense level, criminal history, offense type), extralegal characteristics (e.g., gender, race/ethnicity, citizenship), and case-processing variables (e.g., trial penalty, custody status). The results indicate that various legal and nonlegal factors are relevant in individual cases (representing individual differences) and signify that significant variations across district courts exist (confirming regional disparities). Implications of the significant findings for the justice system are explored.

Risk assessment tools driven by algorithms offer promising advantages in predicting the recidivism risk of defendants.Jurisdictions are increasingly relying upon risk tool outcomes to help judges at sentencing with their decisions on whether to incarcerate or whether to use community-basedsanctions. Yet as sentencing has significant consequences for public safety and individual rights, care must be taken that the tools relied upon are appropriate for the task.Judges are encouraged to act as gatekeepers to evaluate whether the forensic risk assessment tool offered has a sufficient level of validity in that it is fit for the purposes of sentencing, provides an acceptable level of accuracy in its predictions, and achieves an adequate standard of reliability with regard to its outcomes.

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