Towards a 'smart' cost-benefit tool: using machine learning to predict the costs of criminal justice policy interventions.

We argue that the Smart MCBT outlined in this paper will overcome the shortcomings of existing cost-benefit tools. It does this by reintegrating individual cost-benefit analysis (CBA) projects using a database system that securely stores and de-identifies project data, and redeploys it using a range of machine learning and data science techniques. In addition, the question of what works is respecified by the Smart MCBT tool as a data science pipeline, which serves to enhance CBA and reconfigure the policy making process in the paradigm of open data and data analytics.