Key Notes
- Michigan will begin a new prisoner-classification pilot at selected men’s facilities on December 1, using a tool developed with nonprofit Recidiviz.
- Officials project roughly a 30% improvement in predicting future violence, but the announcement does not publish its metric, baseline or validation results.
- Security classifications affect movement, supervision and activities, while the state has not disclosed which parts of the new system use artificial intelligence.
Michigan plans to test a new system for predicting violence risk inside its prisons, with consequences for how much freedom incarcerated people have during their sentences. A person’s security classification helps determine their movement, supervision and access to activities. The pilot is scheduled to begin December 1 at selected men’s facilities.
The Michigan Department of Corrections announced the project on October 28. Developed with criminal justice technology nonprofit Recidiviz, it will incorporate more current information into classification reviews. Officials expect roughly a 30% improvement in predicting future violence, but the announcement does not publish the calculation or supporting validation results.
A Security Level Shapes Daily Life
Michigan uses four general-population security levels: I, II, IV and V. As FOX 2 Detroit outlined, the lower levels offer more movement and activities, while higher levels involve tighter restrictions and closer supervision. There is no Level III in that system.
The classification process therefore has effects well beyond a label in a database. A higher-risk designation can mean a more restrictive daily routine; a lower one can open up opportunities. The state says some rehabilitation programs operate across all general-population levels, while lower classifications can allow broader access to vocational training or higher education.
More frequent reviews could help staff recognize changes sooner, whether those changes suggest growing risk or improved behavior. That is the operational promise behind replacing periodically collected inputs with information that can be analyzed as it arrives. The pilot will have to show whether those faster reviews produce better placements in practice.
The 30% Figure Is a Projection
The department’s forecast concerns predicting violence, not a demonstrated 30% reduction in assaults. It also concerns conditions inside prison, rather than establishing how accurately the system predicts reoffending after release. Those outcomes require different evidence, and the pilot has not yet begun.
The public announcement leaves important measurement questions unanswered. It does not identify the performance metric behind the 30% projection, explain the comparison baseline or provide error rates. Without that information, readers cannot tell how often the tool might flag someone who would not become violent, or miss someone who would.
A forecast can improve on average while still making consequential mistakes about individuals. For this pilot, useful results would show which predictions improved, what happened after staff acted on them and whether errors were concentrated in particular groups. An understandable route for correcting a mistaken record would matter alongside the overall accuracy score.
What Is Actually AI Remains Unclear
Although the rollout has attracted AI coverage, the state’s release does not identify an underlying model or explain which parts of classification use machine learning. It describes a data-driven review system. That is not enough to establish that a generative AI model will determine someone’s security level, or that Michigan is handing prison management to an autonomous agent.
The distinction matters. In an October analysis, the Center for Democracy and Technology explains that many criminal justice risk tools use fixed statistical formulas rather than AI. It separately describes Recidiviz’s use of language models to summarize case notes and draft reentry plans. Those functions do not, by themselves, establish how Michigan’s new risk score is calculated.
Recidiviz has acknowledged the consequences of inaccurate information. Its June safety overview says distorted case notes can affect housing, program eligibility and liberty. It describes phased testing and continuing human review for AI modules. That general account is useful context, but it does not provide a validation report for this Michigan pilot.
The accountability question connects with the challenges we examined in AI evidence: officials need a way to check a technology’s claims before relying on them. Michigan says it will evaluate the tool before considering wider use. Publishing the pilot’s methods and results would let people assess whether faster classification reviews also make prison life safer and decisions more reliable.
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