Assessing the Impact of Justice Reforms with Machine Learning and Advanced Data Analytics

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This article examines how justice processes can be enhanced through the integration of a heterogenous component into a cost-benefit analysis app. This app captures the varying impacts benefactors and beneficiaries feel and requires five key elements to serve its purpose. These elements involve steps beyond previously utilized cost-benefit analyses, such as averages, identifying specific variations in social groups and populations, and utilizing statistically informed techniques to draw new insights from data. Tests were conducted on primary data from a developmental crime prevention intervention in Australia to gauge effects and refine the cost-benefit analysis app. The results revealed a heterogenous distribution of costs and benefits across subgroups and steered the development of the next edition of the app – one that uses AI and modern data science.

The implementation of this app helps enhance justice processes, policy making efficiency, and optimal distribution of criminal justice resources. The cost-benefit app also bolsters better policy accessibility by providing social group-specific data, making the policy-making process more inclusive, just, and resilient.

The company mentioned in this article is the developmental crime prevention intervention organization based in Australia. This organization looks at strengthening social bonds in order to deliberately target potential criminal behaviour. The organization is rooted in the idea that a strong sense of belonging can help protect individuals and the wider community from criminal behaviour.

The person mentioned in this article is a data scientist, who spearheaded the project to develop the cost-benefit analysis app. This produced an app that incorporates artificial intelligence-driven components in order to reintegrate individual cost-benefit analysis projects using machine learning and other modern data science techniques. The role of the data scientist was to refine the app, encompass the variations among social groups, and make sure the costs and benefits were spread out harmoniously for optimal policy orientation.

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