Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions
Abstract 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 ec...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-10-01
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| Series: | Crime Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40163-018-0086-4 |
| _version_ | 1811340487630520320 |
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| author | Matthew Manning Gabriel T. W. Wong Timothy Graham Thilina Ranbaduge Peter Christen Kerry Taylor Richard Wortley Toni Makkai Pierre Skorich |
| author_facet | Matthew Manning Gabriel T. W. Wong Timothy Graham Thilina Ranbaduge Peter Christen Kerry Taylor Richard Wortley Toni Makkai Pierre Skorich |
| author_sort | Matthew Manning |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T18:42:42Z |
| format | Article |
| id | doaj.art-8e172f3689014a1fa3e79476b257d275 |
| institution | Directory Open Access Journal |
| issn | 2193-7680 |
| language | English |
| last_indexed | 2024-04-13T18:42:42Z |
| publishDate | 2018-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Crime Science |
| spelling | doaj.art-8e172f3689014a1fa3e79476b257d2752022-12-22T02:34:41ZengBMCCrime Science2193-76802018-10-017111310.1186/s40163-018-0086-4Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventionsMatthew Manning0Gabriel T. W. Wong1Timothy Graham2Thilina Ranbaduge3Peter Christen4Kerry Taylor5Richard Wortley6Toni Makkai7Pierre Skorich8ANU Centre for Social Research and Methods, Australian National UniversityANU Centre for Social Research and Methods, Australian National UniversityResearch School of Computer Science, Australian National UniversityResearch School of Computer Science, Australian National UniversityResearch School of Computer Science, Australian National UniversityResearch School of Computer Science, Australian National UniversityJill Dando Institute of Security and Crime Science, University College LondonANU Centre for Social Research and Methods, Australian National UniversityAustralian Public ServiceAbstract 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.http://link.springer.com/article/10.1186/s40163-018-0086-4Cost–benefit analysisMachine learningCost–benefit toolsData science |
| spellingShingle | Matthew Manning Gabriel T. W. Wong Timothy Graham Thilina Ranbaduge Peter Christen Kerry Taylor Richard Wortley Toni Makkai Pierre Skorich Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions Crime Science Cost–benefit analysis Machine learning Cost–benefit tools Data science |
| title | Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions |
| title_full | Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions |
| title_fullStr | Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions |
| title_full_unstemmed | Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions |
| title_short | Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions |
| title_sort | towards a smart cost benefit tool using machine learning to predict the costs of criminal justice policy interventions |
| topic | Cost–benefit analysis Machine learning Cost–benefit tools Data science |
| url | http://link.springer.com/article/10.1186/s40163-018-0086-4 |
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