This publication summarizes insights from the "Recidivism Forecasting Challenge Winners Symposium" so they may serve as guidance for tool developers, practitioners, policymakers, and the public; it lays out key findings regarding racial bias and gender responsiveness in risk assessment, design considerations, and increasing awareness.
Although the practice of forecasting recidivism is not new in criminal justice, there is growing interest in incorporating statistical algorithms to predict future criminal behavior among individuals involved in the system. Such predictions can guide decision-making about the appropriateness of pre-trial release, security level and access to programming during incarceration, and levels of supervision following release. These statistical algorithms are referred to as actuarial risk assessments. The ability of these instruments to identify factors that potentially contribute to recidivism may also help reduce mass imprisonment, enhance public safety, and decrease crime. Despite their promise, actuarial risk assessments pose lingering questions and concerns, particularly as methods and measures used to predict recidivism have evolved. Critics argue that the accuracy of these tools may be exaggerated at times and that the tools often lack transparency and fairness. Additionally, both researchers and practitioners debate about the best methods and data with which to predict recidivism, define recidivism, and reduce racial and gender disparities. In 2021, NIJ created the “Recidivism Forecasting Challenge” to advise on these issues related to risk assessment. Following the competition, the National Institute of Justice held a symposium for Challenge winners to share strategies for developing, implementing, and refining risk assessments to address biases and uncertainties. This article summarizes these insights as guidance for tool developers, practitioners, policymakers, and the interested public.