{"resourceId":"ccj-ai-user-decision-framework-202603","versions":[{"version":"external-2decb43fe7e90c2806d7eac8ab2496774953460c27191c41993661b67c9291bd","resource":{"id":"ccj-ai-user-decision-framework-202603","title":"Criminal-justice AI framework ties adoption to validation, procurement gates and annual reassessment","organization":"Council on Criminal Justice Task Force on Artificial Intelligence","sector":"Criminal justice (law enforcement, courts, corrections, community)","geography":"United States (national guidance)","publishedAt":"March 2026","publicationDate":null,"eventDate":null,"sourceName":"Council on Criminal Justice","sourceLabel":"Standards / decision framework","sourceUrl":"https://counciloncj.org/assessing-ai-for-criminal-justice-a-user-decision-framework/","evidenceClass":"standards-guidance","outcomeClass":"emerging","topics":["governance-procurement","operating-model","accessibility-workforce"],"finding":"The March 2026 CCJ user decision framework translates criminal-justice AI principles into a five-phase workflow: problem definition and readiness; prohibited-use screening and risk/opportunity classification; procurement protections; Level 1/Level 2 implementation safeguards (including pilots and override documentation); and ongoing monitoring with at least annual reassessment for substantial-risk systems. It explicitly requires independent validation rather than vendor claims alone and addresses staff use of general-purpose AI tools. It does not present measured outcome results from a specific deployment.","sledRelevance":"Directly usable by U.S. state and local public-safety agencies evaluating police, court, or corrections AI procurements. Provides shared language for sales, engineering, and delivery to structure go/no-go gates without inventing ROI.","evidence":"Full framework text retrieved from the CCJ publication page and cross-checked against the March 2026 PDF introduction/principles and phase structure.","architectureImplications":"Interpretation: build for explainability/override paths, audit trails, and monitoring hooks before integration with RMS/CAD/court CMS/supervision systems; treat general-purpose chat tools as a separate governance surface from purpose-built procured systems.","governanceImplications":"Interpretation: require classification memos, multidisciplinary teams, community input for substantial-risk tools, and formal go/no-go after pilots. Annual reassessment should be budgeted, not optional.","securityPrivacyImplications":"Procurement and implementation phases emphasise data rights, confidentiality, least collection, and access controls for sensitive justice data; contracts should encode auditability and termination rights.","caveats":"Guidance only; Microsoft listed among funders; local capacity may not support full Level 2 controls; promised 2026 case studies not yet available in this inspection.","streamIds":["public-safety"],"roles":{"sales":"Lead with decision hygiene for criminal-justice AI: problem definition, risk class, and independent validation—not product demos alone. The CCJ framework gives buyers a credible checklist spanning police, courts, and corrections. Discovery should test whether the agency can staff a multidisciplinary assessment and enforce contract terms. Bound the engagement to a classification memo and procurement gap review; do not promise that following the framework guarantees fair or effective outcomes.","engineering":"Fit is process architecture for AI adoption: readiness assessment, risk/opportunity classification, and implementation controls (override UX, logging, monitoring). Integrate proof points and demographic performance reporting into the operational stack. Prerequisites include legal/constitutional review capacity and data governance basics. Constraints: substantial-risk liberty-impacting tools need Level 2 safeguards that many small agencies cannot fully staff without shared services. Security must cover sensitive case data and vendor access. Proof of value: completed classification memo and pilot metrics against a pre-registered success measure.","delivery":"Delivery is a gated programme: finish Phase 1–2 before buying; negotiate Level 1/2 contract terms; run a scoped pilot with go/no-go; then operate monitoring and annual reassessment. Dependencies include counsel, IT, end users, and community representatives for substantial-risk tools. Ownership sits with agency leadership approving classification and deployment. Skills include automation-bias training and documentation discipline. Acceptance criteria are process completions and pilot success metrics—not vendor marketing KPIs. Risks include informal chat-tool use bypassing procurement."},"roleDetails":{"sales":{"customerProblem":"Agencies lack a shared, justice-specific path to decide which AI tools to buy, pause, or refuse.","stakeholders":["agency executives","procurement/legal","IT/project managers","community advisors","oversight bodies"],"discoveryQuestions":["Has the problem and non-AI alternatives been documented before vendor selection?","Who approves substantial-risk classification memos?","Can contracts require independent validation, audit logs, and termination rights?"],"valueHypothesis":"Using the CCJ phases reduces avoidable procurement and rights risk by forcing validation and go/no-go gates before scale.","potentialEngagement":"Facilitated readiness and classification workshop producing a draft memo and procurement checklist tailored to one use case.","unsupportedClaims":["That the framework itself improves rearrest or clearance rates","Guaranteed fairness after checklist completion","Fitness of any specific vendor product"]},"engineering":{"fit":"Purpose-built criminal-justice AI under formal procurement, plus policy design for general-purpose staff AI tools.","architectureIntegration":"Design recommendation UIs with override capture, demographic performance dashboards, and exportable audit trails into existing justice systems.","prerequisites":["Data governance basics","Legal review capacity","Defined success metrics and baselines"],"constraints":["Small-agency capacity limits for Level 2 controls","Black-box systems may force higher risk class","Framework is advisory, not regulation"],"security":"Encode confidentiality, access control, and data-rights terms before integration; prevent silent repurposing of collected data.","proofOfValue":["Independent validation plan on representative local data","Pilot compares AI to current practice on pre-registered metrics","Override logs reviewable for legal/appeals use"]},"delivery":{"work":"Run the five-phase workflow for a named use case, including pilot evaluation and monitoring setup.","dependencies":["procurement","counsel","end-user supervisors","community engagement lead for substantial risk"],"ownership":"Agency head or designee for classification approval and deployment authorization.","skillsAdoption":"Mandatory user training on limitations and automation bias, with refreshers on model change.","governanceCheckpoints":["Phase exit approvals","Contract execution before implementation","Annual reassessment for substantial-risk systems"],"acceptanceCriteria":["Classification memo filed with risk/opportunity rationale","Pilot go/no-go documented against success metrics","Monitoring produces error and demographic performance reports"],"risks":["Shadow use of general-purpose AI on case data","Vendor resistance to audit/termination terms","Checklist completion mistaken for outcome proof"]}},"retrievedAt":"2026-09-15T14:14:12Z","enrichedAt":"2026-09-15T14:17:25Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Mandatory training and continuing education on system limits are core; community advisory participation for substantial-risk tools requires time and possibly compensation.","procurementImplications":"Phase 3 treats contracts as the safety net: performance standards, fairness, auditability, data rights, and termination should be negotiated before acquisition.","operatingModelImplications":"Multidisciplinary assessment and annual reassessment become standing operating functions, not one-off project tasks; informal chatbot use needs a parallel policy.","sourceVerification":{"openedUrl":"https://counciloncj.org/assessing-ai-for-criminal-justice-a-user-decision-framework/","referenceExcerpt":"agencies should require rigorous, independent validation rather than relying solely on vendor claims","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}