Smart Justice? Making Sense of the Rise of Algorithm-Based Pre-trial Risk Assessment in Criminal Justice Through ‘Legal Models’

Abstract With the increased use of algorithmic tools embedded in software by state actors, scholars from criminology, criminal justice as well as from data science have analyzed the recent wave of ‘smart-on-crime’-politics in the US and the political dynamics underpinning this movement. However, while reforms of the US bail system have been studied extensively, we know little about how these reforms, including the recent embrace of digital risk prediction tools, reflect shifting commitments to underlying principles of the CJ system. Therefore, this article interprets the waves of US bail reforms through the application of three legal-theoretical models: ‘retributive justice’ (RJ), ‘actuarial justice’ (AJ) and ‘preventive justice’ (PJ). This conceptual lens enables us to illuminate how the increased use of pre-trial risk assessment tools based on big data can be understood in legal-theoretical terms. Empirically, we find a shift away from censure and retribution towards crime prevention and the use of risk assessment tools, which both AJ and PJ models can accommodate. However, while our analysis demonstrates that these models help draw into sharper focus the principles and values which animated US bail reforms, it also reveals several limitations owing to the nature of these models as witnesses of the time when they were developed.