{"resourceId":"urban-responsible-ai-corrections-2026","versions":[{"version":"external-85f042b2f8a4861420234196a1b9b011a046285b9cfc8df113dd30172713d1e0","resource":{"id":"urban-responsible-ai-corrections-2026","title":"Corrections brief calls for bounded pilots and independent oversight","organization":"Urban Institute; David Pitts and KiDeuk Kim","sector":"Prisons, jails and reentry","geography":"United States","publishedAt":"Landing page dated February 4, 2026; PDF carries conflicting dates","publicationDate":"2026-02-04","eventDate":null,"sourceName":"Urban Institute","sourceLabel":"Policy brief, not an impact evaluation","sourceUrl":"https://www.urban.org/research/publication/responsible-ai-adaptation-corrections","evidenceClass":"standards-guidance","outcomeClass":"emerging","topics":["knowledge-work","infrastructure","data-security","governance-procurement","accessibility-workforce","operating-model"],"finding":"The brief proposes limited corrections AI pilots with safeguards addressing bias, privacy and opaque systems.","sledRelevance":"New-to-archive baseline guidance for state corrections agencies and county jails, rather than evidence of achieved rehabilitation or safety gains.","evidence":"The publication discusses administrative assistance and reentry applications and recommends oversight, data governance and independent evaluation. It provides no deployment comparison, measured benefit sample or causal impact estimate.","architectureImplications":"Interpretation: Start with an isolated administrative workflow; assess legacy interfaces, connectivity, source permissions and offline fallback. No validated cloud, hybrid or on-premises reference architecture is supplied.","governanceImplications":"Interpretation: Classify consequences before labeling administrative work low-risk; housing and incident records can affect liberty and treatment.","securityPrivacyImplications":"Interpretation: Minimize incarcerated-person and family information; contractually constrain training reuse and third-party access.","caveats":"Recommendations are normative. The landing page dates publication February 4, 2026, but PDF cover says February 2025 and copyright says December 2025. Exact PDF issue date remains unresolved.","streamIds":["public-safety"],"roles":{"sales":"Interpretation: Corrections administrators, frontline staff, incarcerated people, families and oversight bodies have different priorities that a pilot must reconcile. Ask which administrative task consumes time, whether its output influences placement or discipline, and who benefits if work is reduced. A bounded engagement could examine visitor scheduling assistance with a manual alternative. The value hypothesis is reliable service with less clerical effort, to be tested locally. The brief does not demonstrate savings, reduced self-harm or lower recidivism. Do not turn its potential use cases into verified customer outcomes or assume that a routine-sounding workflow has negligible consequences.","engineering":"Interpretation: Fit should be determined through a data and consequence assessment before product selection. Map approved source systems, identities, retention, exception queues and human authority. Prerequisites include representative records, known data defects, connectivity testing and an operational fallback. Keep development and agent experiments on synthetic or approved de-identified records until security review is complete. Test incorrect identities, stale records, denied access and provider outage. A proof of value should compare the current service with the pilot using independent reviewers, subgroup error analysis and staff effort. Avoid connecting an unvalidated assistant to autonomous placement, discipline or eligibility decisions.","delivery":"Interpretation: A designated facility service manager should own delivery with IT, records, staff representatives and independent evaluators. Include affected people in workflow design and explain how errors can be corrected. Dependencies include data cleanup, staff training and procurement terms that allow meaningful scrutiny. Review consequence classification before launch and again before expanding scope. Proposed acceptance criteria include correct processing of an agreed test set, no unexplained subgroup degradation, successful fallback and appeal exercises, and documented service improvement rather than merely faster output. Publish a balanced pilot assessment where permitted. Risks include function creep, stale data and resources diverted into monitoring instead of services."},"retrievedAt":"2026-09-07T03:01:50Z","enrichedAt":"2026-09-07T03:01:50Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Interpretation: Test staff usability and access for incarcerated people with disability or language needs; retain human assistance.","procurementImplications":"Interpretation: Prefer a time-bounded evaluation contract with data export, independent test access and explicit termination provisions.","operatingModelImplications":"Interpretation: Tie any saved capacity to a service objective and independently measure whether that service improves.","sourceVerification":{"openedUrl":"https://www.urban.org/research/publication/responsible-ai-adaptation-corrections","referenceExcerpt":"Risks center on data quality, bias, and opaque systems.","promptVersion":"sled-research-v3.1","model":null,"basis":"agent-reported inspection"}}}]}