From the Public Safety edition of November 2, 2026
Recidiviz says Michigan’s new prison classification tool is points-based, not AI, and sets out its review and error-ceiling rules for generative AI case notes
Recidiviz (nonprofit) with Michigan Department of Corrections · Corrections / classification instruments and generative AI case notes · Michigan and other U.S. states
- Publisher
- Recidiviz
- Original publication
- Recidiviz blog, Nov. 2, 2026
- Source retrieved
- 2026-11-03
What happened
Recidiviz, the nonprofit working with the Michigan Department of Corrections on its classification pilot due Dec. 1, wrote on Nov. 2, 2026 that coverage describing the tool as AI was wrong: both the old and new instruments are rules-based points systems staff can read line by line. It says the redesign analyzed 10 years of Michigan classification data and that MDOC staff experts decided which factors to keep. Recidiviz says it does not use AI to decide custody, parole or release, or to score risk, and that AI only drafts records that staff review and submit. For its generative AI case-note tool, it says staff manually review 100% of outputs in early testing, a second model grades every output later, harmful errors have a strict ceiling, and tools are rolled back if the ceiling is crossed.
Why it matters
A provider correcting AI framing of a corrections risk tool, and publishing its own generative AI control rules, which buyers can turn into contract terms.
Evidence and measured results
Opened and read the full post. All statements are the provider’s own; no error rates, ceiling values or validation results are disclosed. MDOC’s Oct. 28 release was archived previously and is not reused.
Limitations and uncertainty
Provider self-description; the 30% improvement claimed by MDOC remains unexplained; no independent validation.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-11-03; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Corrections agencies face confusion and distrust about whether tools use AI to decide custody or release.
- Stakeholders
- Corrections classification director
- Case-management lead
- Legislative oversight
- Advocates for incarcerated people
- Discovery questions
- Is this tool AI, rules-based, or statistical?
- How was the instrument validated on our data?
- What is the harmful-error ceiling for AI drafts?
- Value hypothesis
- Clear method disclosure and output evaluation build trust in corrections tools.
- Potential engagement
- Tool method documentation and evaluation design for corrections classification and AI drafting.
- Unsupported claims
- Michigan 30% violence prediction improvement
- Error rates not disclosed
- Independent validation
Pre-sales engineering
Role takeaway
- Fit
- Relevant to corrections agencies redesigning classification or adopting AI case notes.
- Architecture and integration
- Points-based instrument in the offender management system; generative AI drafting from meeting transcripts with a grader model.
- Prerequisites
- Historical classification data
- Transcripts of supervision meetings
- Error taxonomy
- Constraints
- Data quality
- Model change control
- State law on records authorship
- Security
- Restrict drafting tool data to the case, keep officer authorship, log every output and grade.
- Proof of value
- Subgroup validation of the instrument
- Draft accuracy sample against transcripts
- Rollback drill
Delivery
Role takeaway
- Work
- Document instrument factors, validate, and run AI drafting with full output evaluation.
- Dependencies
- Provider evaluation data
- Staff working group
- Oversight review
- Ownership
- Corrections classification director and case-management program lead.
- Skills and adoption
- Officers need training to edit drafts substantively; staff need to explain scores line by line.
- Governance checkpoints
- Instrument factors published
- Validation reviewed
- Harmful-error ceiling set
- Acceptance criteria
- Subgroup validation reported
- Harmful-error rate below ceiling
- Rollback completed on breach
- Risks
- Rubber-stamp review
- Unexplained predictive claims
- Bias in historical data
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Transparent points-based instrument for classification; generative AI limited to draft case notes with a second-model grader.
Governance
Who approves, reviews and stays accountable for outcomes?
Separates decision tools from drafting tools; human authorship of records; rollback on harmful-error ceiling.
Security and privacy
What data, permissions and controls need testing?
Case notes contain sensitive supervision data; the post does not detail data handling.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Officers remain legal authors and must spend real time editing drafts.
Procurement
What should contracts, pricing and exit terms secure?
Buyers can require disclosure of whether a tool is AI, evaluation of every output, and rollback terms.
Operating model
Which teams own the service once it runs?
Every model or prompt change treated as a new launch with re-evaluation.
Publication history
- 2026-11-02Public Safety · Issue 575 resources
Stable resource ID: recidiviz-clarifies-michigan-classification-not-ai-generative-ai-safeguards-corrections-20261102