Hope, Hype, and Fear: The Promise and Potential Pitfalls of Artificial Intelligence in Criminal Justice

Isaac, William S
Ohio State Journal of Ciminal Law

Over the past decade, algorithmic decision systems (ADSs)—applications of statistical or computational techniques designed to assist human-decision making
processes—have moved from an obscure domain of statistics and computer science
into the mainstream.
The rapid decline in the cost of computer processing and ubiquity of digital data storage have created a dramatic rise in the adoption of ADSs using applied machine learning algorithms, transforming various sectors of society from digital advertising to political campaigns, risk modeling for the banking sector,
health care and beyond. In particular, many practitioners in the public sector have begun turning to ADSs as a means to stretch limited public resources amidst growing public demands for equity and accountability.
Advocates of these “intelligence-led” or “evidence-based” policy approaches assume big data tools will allow government agencies to use objective data to
overcome historical inequalities to better serve underrepresented groups.