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Actuarial risk assessments might be unduly perceived as a neutral way tocounteract implicit bias and increase the fairness of decisions made at almostevery juncture of the criminal justice system, from pretrial release tosentencing, parole and probation. In recent times these assessments have comeunder increased scrutiny, as critics claim that the statistical techniquesunderlying them might reproduce existing patterns of discrimination andhistorical biases that are reflected in the data. Much of this debate iscentered around competing notions of fairness and predictive accuracy, restingon the contested use of variables that act as "proxies" for characteristicslegally protected against discrimination, such as race and gender. We arguethat a core ethical debate surrounding the use of regression in riskassessments is not simply one of bias or accuracy. Rather, it's one of purpose.If machine learning is operationalized merely in the service of predictingindividual future crime, then it becomes difficult to break cycles ofcriminalization that are driven by the iatrogenic effects of the criminaljustice system itself. We posit that machine learning should not be used forprediction, but rather to surface covariates that are fed into a causal modelfor understanding the social, structural and psychological drivers of crime. Wepropose an alternative application of machine learning and causal inferenceaway from predicting risk scores to risk mitigation.

Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment