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

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dc.contributor.authorMahony, Chris
dc.contributor.authorManning, Matthew
dc.contributor.authorWong, Gabriel
dc.date.accessioned2023-05-22T18:15:23Z
dc.date.available2023-05-22T18:15:23Z
dc.date.issued2023-05-22
dc.description.abstractCan the impact of justice processes be enhanced with the inclusion of a heterogeneous component into an existing cost-benefit analysis app that demonstrates how benefactors and beneficiaries are affected Such a component 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 processes differ 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 for beneficiaries. This paper outlines a method for capturing heterogeneity. The paper tests the method and the cost-benefit analysis online app that was developed using primary data collected from a developmental crime prevention intervention in Australia. The paper identifies how subgroups in the intervention display different behavioral adjustments across the reference period, revealing the heterogeneous distribution of costs and benefits. Finally, the paper discusses the next version of the cost-benefit analysis app, which incorporates an artificial intelligence-driven component that reintegrates individual cost-benefit analysis projects using machine learning and other modern data science techniques. The paper argues that the app enhances cost-benefit analysis, development outcomes, and policy making efficiency for optimal prioritization of criminal justice resources. Further, the app advances the policy accessibility of enhanced, social group-specific data, illuminating optimal policy orientation for more inclusive, just, and resilient societal outcomes—an approach with potential across broader public policy.en
dc.identifierhttp://documents.worldbank.org/curated/en/099440005182319271/IDU045e8fb0a02d11043670892b07982da772324
dc.identifier.doi10.1596/1813-9450-10449
dc.identifier.urihttps://openknowledge.worldbank.org/handle/10986/39832
dc.languageEnglish
dc.language.isoen
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Papers; 10449
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectJUSTICE REFORM
dc.subjectCOST-BENEFIT ANALYSIS
dc.subjectMACHINE LEARNING
dc.subjectDATA SCIENCE
dc.subjectJUSTICE PROCESSES
dc.subjectHETEROGENEITY
dc.titleWeighting Justice Reform Costs and Benefits Using Machine Learning and Modern Data Scienceen
dc.typeWorking Paper
dspace.entity.typePublication
okr.crossref.titleWeighting Justice Reform Costs and Benefits Using Machine Learning and Modern Data Science
okr.date.disclosure2023-05-18
okr.date.lastmodified2023-05-18T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099440005182319271/IDU045e8fb0a02d11043670892b07982da772324
okr.guid099440005182319271
okr.identifier.docmidIDU-45e8fb0a-2d11-4367-892b-7982da772324
okr.identifier.doi10.1596/1813-9450-10449
okr.identifier.doihttp://dx.doi.org/10.1596/1813-9450-10449
okr.identifier.externaldocumentum34064703
okr.identifier.internaldocumentum34064703
okr.identifier.reportWPS10449
okr.import.id745
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099440005182319271/pdf/IDU045e8fb0a02d11043670892b07982da772324.pdfen
okr.region.countryAustralia
okr.sectorSocial Protection
okr.topicLaw and Development::Justice for the Poor
okr.topicLaw and Development::Law and Justice Institutions
okr.topicInformation and Communication Technologies::ICT Applications
okr.topicSocial Development::Social Inclusion & Institutions
okr.unitSocial ECA (SCASO)
okr.unitEFI-FCI-Risk Finance (EFNRF)
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