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To investigate this, we obtained conviction data from the UK\u2019s Police National Computer (PNC) on 346,685 records between January 1, 2000, and February 3, 2006 (His Majesty\u2019s Inspectorate of Constabulary in Use of the Police National Computer: An inspection of the ACRO Criminal Records Office. His Majesty\u2019s Inspectorate of Constabulary, Birmingham, <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/assets-hmicfrs.justiceinspectorates.gov.uk\/uploads\/police-national-computer-use-acro-criminal-records-office.pdf\" ext-link-type=\"uri\">https:\/\/assets-hmicfrs.justiceinspectorates.gov.uk\/uploads\/police-national-computer-use-acro-criminal-records-office.pdf<\/jats:ext-link>, 2017). We generate twelve machine learning models\u2014six to forecast general recidivism, and six to forecast violent recidivism\u2014over a 3-year period, evaluated via fivefold cross-validation. Our best-performing models outperform the existing state-of-the-arts, receiving an area under curve (AUC) score of 0.8660 and 0.8375 for general and violent recidivism, respectively. Next, we construct a fairness scale that communicates the semantic and technical trade-offs associated with debiasing a criminal justice forecasting model. We use this scale to debias our best-performing models. Results indicate both models can achieve all five fairness definitions because the metrics measuring these definitions\u2014the statistical range of recall, precision, positive rate, and error balance between demographics\u2014indicate that these scores are within a one percentage point difference of each other. Deployment recommendations and implications are discussed. These include recommended safeguards against false positives, an explication of how these models addressed societal biases, and a case study illustrating how these models can improve existing criminal justice practices. That is, these models may help police identify fewer people in a way less impacted by structural bias while still reducing crime. A randomized control trial is proposed to test this illustrated case study, and further directions explored.<\/jats:p>","DOI":"10.1007\/s00521-025-11478-x","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T06:19:45Z","timestamp":1754029185000},"page":"21607-21657","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A fairness scale for real-time recidivism forecasts using a national database of convicted offenders"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1790-0748","authenticated-orcid":false,"given":"Jacob","family":"Verrey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Neyroud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lawrence","family":"Sherman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barak","family":"Ariel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"11478_CR1","unstructured":"His Majesty\u2019s Inspectorate of Constabulary (2017) Use of the Police National Computer: An inspection of the ACRO Criminal Records Office. 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