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Towards a 'smart' cost-benefit tool: using machine learning to predict the costs of criminal justice policy
Matthew Manning1, Gabriel T W Wong1, Timothy Graham2
11ANU Centre for Social Research and Methods, Australian National University, Canberra, Australia.
Background:
The Manning Cost-Benefit Tool (MCBT) was developed to assist criminal justice policymakers, policing organisations and crime prevention practitioners to assess the benefits of different interventions for reducing crime and to select those strategies that represent the greatest economic return on investment.
Discussion:
A challenge with the MCBT and other cost-benefit tools is that users need to input, manually, a considerable amount of point-in-time data, a process that is time consuming, relies on subjective expert opinion, and introduces the potential for data-input error. In this paper, we present and discuss a conceptual model for a 'smart' MCBT that utilises machine learning techniques.
Summary:
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.
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