Weighting Justice Reform Costs and Benefits Using Machine Learning and Modern Data Science

Can the impact of justice processes be enhanced with the inclusion of a heterogeneous componentinto an existing cost-benefit analysis app that demonstrates how benefactors and beneficiaries are affected Such acomponent requires (i) moving beyond the traditional cost-benefit conceptual framework of utilizing averages,(ii) identification of social group or population-specific variation, (iii) identification of how justice processesdiffer across groups/populations, (iv) distribution of costs and benefits according to the identified variations, and (v)utilization of empirically informed statistical techniques to gain new insights from data and maximize the impact forbeneficiaries. This paper outlines a method for capturing heterogeneity. The paper tests the method and thecost-benefit analysis online app that was developed using primary data collected from a developmental crime preventionintervention in Australia. The paper identifies how subgroups in the intervention display different behavioraladjustments across the reference period, revealing the heterogeneous distribution of costs and benefits. Finally,the paper discusses the next version of the cost-benefitanalysis app, which incorporates an artificial intelligence-driven component that reintegrates individualcost-benefit analysis projects using machine learning and other modern data science techniques. The paper argues thatthe app enhances cost-benefit analysis, development outcomes, and policy making efficiency for optimalprioritization of criminal justice resources. Further, the app advances the policy accessibility of enhanced, socialgroup-specific data, illuminating optimal policy orientation for more inclusive, just, and resilient societal outcomes—anapproach with potential across broader public policy.