{"resourceId":"michigan-doc-recidiviz-data-driven-classification-pilot-violence-risk-20261028","versions":[{"version":"external-76a79ce4223be5374be9786e26020d117597fbc53e08ecec3d4dcd1d514bed82","resource":{"id":"michigan-doc-recidiviz-data-driven-classification-pilot-violence-risk-20261028","title":"Michigan Department of Corrections will pilot a Recidiviz-built, data-driven prisoner classification tool on Dec. 1, claiming about 30% better prediction of violence risk","organization":"Michigan Department of Corrections / Recidiviz","sector":"Corrections / risk classification and placement","geography":"Michigan, United States","publishedAt":"Michigan Department of Corrections press release, Oct. 28, 2026","publicationDate":"2026-10-28","eventDate":"2026-12-01","sourceName":"Michigan Department of Corrections","sourceLabel":"New Prisoner Classification System Announced by Michigan Department of Corrections","sourceUrl":"https://www.michigan.gov/corrections/about/newsroom/press-releases/2026/10/29/new-prisoner-classification-system-announced-by-michigan-department-of-corrections","evidenceClass":"government-evaluation","outcomeClass":"emerging","topics":["data-security","governance-procurement","operating-model"],"finding":"The Michigan Department of Corrections (MDOC) announced on Oct. 28, 2026 that it will pilot a redesigned prisoner classification system at selected men’s facilities from Dec. 1, 2026, developed with Recidiviz, a nonprofit technology organization that works with criminal justice agencies in 19 states. MDOC says the system is expected to improve its ability to predict incarcerated individuals’ risk of future violence by approximately 30% and will allow ongoing classification reviews using real-time data rather than inputs collected on a schedule. Director Heidi Washington described it as using evidence-based and historical data to create a tool that is “dynamic, objective”. MDOC supervises about 32,000 people across four general-population security levels; classification determines movement, supervision and access to programming such as vocational training and higher education. The department says the pilot will test how well the tool performs before wider use.","sledRelevance":"Classification scores decide daily liberty and program access for thousands of people; a claimed predictive gain announced without method, baseline or fairness testing is exactly the kind of corrections algorithm buyers and oversight bodies need to probe.","evidence":"curl returned HTTP 403; retrieved the press release with the WebFetch tool and read it in full. No technical report, validation study or fairness analysis was linked or found.","architectureImplications":"Shifts classification from scheduled inputs to continuous review on real-time data, implying data pipelines from incident, behaviour and program systems into the scoring tool.","governanceImplications":"Pilot before wider use is stated, but evaluation criteria, independent validation and review rights for incarcerated people are not described.","securityPrivacyImplications":"Real-time behavioural data feeding classification raises data-quality and due-process questions; the release does not describe data sources.","caveats":"Agency press release; the 30% figure is a projection with no stated baseline, metric or validation method; no fairness or disparate-impact testing is described; the tool is not described as AI, so it is classified here as an algorithmic risk instrument.","streamIds":["public-safety"],"roles":{"sales":"Sales framing: Michigan is piloting a data-driven classification tool that it says predicts violence risk about 30% better, and those scores decide movement and program access for people in prison. The buyer problem is showing that claim holds up and is fair before wider use. A bounded engagement could design the pilot’s independent validation plan. The evidence cannot support the 30% figure as measured, any fairness claim, or safety outcomes; the press release gives no method, baseline or sample.","engineering":"Engineering framing: moving from scheduled inputs to real-time data means continuous feeds from incident, misconduct and programming systems, and each feed’s error rate becomes a classification error. Pre-sales should ask what the 30% measures (for example AUC or precision at a cut-off), against what baseline, and how scores are explained and overridden. A proof of value would back-test the tool on historical cohorts by group. No technical documentation was published with the announcement.","delivery":"Delivery framing: the work is running a pilot that can be evaluated: pre-registered metrics, override tracking, group-level error analysis and a route for incarcerated people to contest a classification. The operational owner is the MDOC classification director’s office. Acceptance criteria are proposals: published validation with error rates by group, override reasons logged for every case, and a decision gate before expansion. Risks include data errors raising security levels, opaque scores, and expansion before evaluation."},"roleDetails":{"sales":{"customerProblem":"A corrections department must show that a new data-driven classification tool improves violence prediction fairly before scores shape placement for 32,000 people.","stakeholders":["MDOC director and classification office","Recidiviz","Facility wardens and classification staff","Legislative oversight","Advocates for incarcerated people"],"discoveryQuestions":["What does the 30% improvement measure, and against which baseline?","How do error rates differ by race, age and facility?","Can staff override, and are overrides logged?","How can a person contest a classification?"],"valueHypothesis":"Interpretation: an independent validation plan could make the pilot’s results credible to oversight bodies.","potentialEngagement":"A pilot evaluation design covering validation metrics, fairness analysis and override monitoring.","unsupportedClaims":["The 30% figure as an independently measured result","That the tool is unbiased","That it will reduce assaults"]},"engineering":{"fit":"Relevant to state corrections agencies revising classification instruments.","architectureIntegration":"Scoring tool fed by real-time incident, behaviour and programming data; outputs drive security-level placement.","prerequisites":["Data-quality checks on each feed","Defined metrics and baseline","Override workflow"],"constraints":["Pilot at selected men’s facilities only","No published technical documentation","Projection, not observed result"],"security":"Protect incarcerated people’s data, log score changes and overrides, and restrict model changes to governed releases.","proofOfValue":["Back-test on historical cohorts","Compare error rates by group","Audit a sample of overrides"]},"delivery":{"work":"Run an evaluable pilot with fairness analysis and a contest route.","dependencies":["Recidiviz model documentation","Data feeds","Evaluation partner"],"ownership":"MDOC classification director’s office","skillsAdoption":"Training classification staff to interpret scores and record override reasons.","governanceCheckpoints":["Pre-registered evaluation metrics","Mid-pilot fairness review","Expansion decision gate"],"acceptanceCriteria":["Published validation with group error rates before expansion (proposed)","Override reasons logged for 100% of overrides (proposed)","Contest route available to every pilot participant (proposed)"],"risks":["Data errors raising security levels","Opaque scoring","Expansion before evaluation"]}},"retrievedAt":"2026-10-29T02:52:38Z","enrichedAt":"2026-10-29T02:57:15Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Classification affects access to programming and barrier-free housing; staff will need to understand and override scores.","procurementImplications":"Developed with a nonprofit partner rather than a commercial vendor; ownership of the model and data is not stated.","operatingModelImplications":"Continuous re-classification could change movement and programming decisions more often than scheduled reviews.","sourceVerification":{"openedUrl":"https://www.michigan.gov/corrections/about/newsroom/press-releases/2026/10/29/new-prisoner-classification-system-announced-by-michigan-department-of-corrections","referenceExcerpt":"MDOC is launching this pilot to test how well the new tool performs before considering wider use.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}