{"resourceId":"ori-generative-ai-research-misconduct-guidance-202608","versions":[{"version":"external-0e5b94862e225418af94b5957e9118a968f99ebe6c5d4358fb4e0a63f154e21b","resource":{"id":"ori-generative-ai-research-misconduct-guidance-202608","title":"ORI Aug 2026 generative AI guidance: disclose AI use, verify results; detection tools not sole misconduct evidence","organization":"U.S. Department of Health and Human Services Office of Research Integrity (ORI)","sector":"Federal research integrity / PHS-funded research administration","geography":"United States (PHS-funded institutions nationwide)","publishedAt":"August 2026 (issuance); ORI release page Aug 14, 2026","publicationDate":"2026-08-14","eventDate":null,"sourceName":"ORI Guidance on Generative Artificial Intelligence (42 CFR Part 93)","sourceLabel":"Federal guidance (HHS ORI)","sourceUrl":"https://ori.hhs.gov/sites/default/files/2026-08/ORI%20Generative%20Artificial%20Intelligence%20Guidance_final.pdf","evidenceClass":"standards-guidance","outcomeClass":"emerging","topics":["governance-procurement","data-security","operating-model"],"finding":"ORI’s August 2026 nonbinding guidance explains how generative AI interacts with PHS research misconduct rules under 42 CFR Part 93 (2024). Researchers should identify generative AI tools used in research and in manuscript/grant preparation and verify results, including AI-derived outputs. Disclosure is not a safe harbor if use significantly departs from accepted practices of the relevant research community. Institutions should gather all available evidence and are advised against relying on AI detection tools as the sole source of evidence; plagiarism detectors may miss AI-generated scientific text. Institutional records and sequestered evidence are generally retained seven years. Why now: same-week physical instrument agents and embodied-lab benchmarks make disclose/verify/preserve controls operationally binding for research administration.","sledRelevance":"Research administration and RIOs adopting AI-assisted discovery need federal misconduct-aligned disclosure, verification, and record-retention practices that transfer across PHS-funded campuses.","evidence":"ORI PDF guidance opened via HTTPS; disclose/verify recommendations, non-sole-reliance on detection tools, and Part 93 framing taken from opened PDF (Date of Issuance August 2026; release page Aug 14).","architectureImplications":"Prompt/version/output retention and research-record systems must capture generative AI use trails usable in inquiry/investigation sequestration.","governanceImplications":"RIO-led proceedings under Part 93; optional institutional generative-AI policies may help define accepted practices; funder AI policies may inform community norms.","securityPrivacyImplications":"Evidence sequestration and seven-year retention apply to AI session artifacts when they are research records; coordinate with IRB/IACUC retention policies.","caveats":"Nonbinding guidance; August date older than last edition—included for why-now control-layer relevance; does not evaluate scientific agent performance; no published compliance KPIs.","streamIds":["research"],"roles":{"sales":"Sales framing: Research-admin and compliance buyers need a federal control narrative for generative AI—disclose, verify, and preserve—not another model license. ORI’s August 2026 guidance is a transferable PHS misconduct pattern newly salient as instrument agents and embodied-lab systems land on campuses this week. Do not claim measured misconduct reduction from policy text alone; this is guidance-design evidence for discovery conversations with RIOs and VPRs.","engineering":"Engineering framing: Fit requires wiring AI-use disclosure fields, prompt and output retention, and verification checkpoints into grant, manuscript, and agent platforms so research records remain sequesterable under Part 93 timelines. Prerequisites include research-record definitions covering AI artifacts and RIO-accessible storage. This interpretation is bounded to the retrieved nonbinding guidance and should not be treated as a rewrite of the underlying regulation or a guarantee of ORI findings.","delivery":"Delivery framing: Update RIO playbooks, train PIs on disclosure and verification, and ensure AI session logs meet seven-year retention when they qualify as research records. Acceptance includes live disclosure templates and inquiry SOPs that forbid relying on AI detection tools as the sole evidence. Risks include disclosure theater without verification, shadow AI tools outside logging, and incomplete prompt histories that break sequestration during proceedings."},"roleDetails":{"sales":{"customerProblem":"PHS-funded units adopt generative AI and research agents faster than misconduct-aligned disclosure, verification, and record practices.","stakeholders":["Research Integrity Officer / ORIA","VPR / sponsored research","Compliance / counsel","PIs and lab managers","Library / research data services"],"discoveryQuestions":["Where must generative AI use be disclosed in grants and manuscripts?","How are AI prompts/outputs retained as research records?","Are AI detectors used as sole misconduct evidence?","Do institutional AI policies define accepted practices for committees?"],"valueHypothesis":"Aligning campus AI research workflows to ORI disclose/verify/preserve guidance can reduce uncontrolled generative-AI use risk in PHS-funded settings.","potentialEngagement":"Gap assessment of disclosure templates, AI artifact retention, and inquiry SOPs against ORI generative-AI guidance.","unsupportedClaims":["Measured drop in misconduct findings","Safe harbor from disclosure alone","Binding mandate for standalone AI policies","Scientific quality improvement from policy alone"]},"engineering":{"fit":"Research platforms and agent gateways that capture AI tool identity, prompts/parameters/versions, and human verification attestations in the research record.","architectureIntegration":"Connect grant/manuscript systems, LIMS/ELN, and agent logs to sequesterable storage with RIO access paths under Part 93 timelines.","prerequisites":["Research-record definition covering AI artifacts","Disclosure fields","Retention schedule (≥7 years when applicable)","RIO access procedures","Training"],"constraints":["Nonbinding guidance","Community-practice dependence","August issuance age","No KPI dashboard in the guidance"],"security":"Protect sequestered AI evidence; control access during inquiry/investigation; coordinate with IRB/IACUC retention.","proofOfValue":["Disclosure appearing in sample outputs","AI artifact retention tests","Inquiry SOP forbids detector-only findings","Training completion for PIs/RIOs"]},"delivery":{"work":"Publish disclosure templates, update retention SOPs, train RIOs/PIs, revise inquiry checklists.","dependencies":["Sponsored research systems","ELN/LIMS vendors","Counsel","College research offices"],"ownership":"RIO owns misconduct process; VPR owns research policy; units own local disclosure compliance.","skillsAdoption":"Train PIs to disclose and verify; train committees on AI subject-matter consultation and evidence breadth.","governanceCheckpoints":["Policy/guidance crosswalk","Retention audit","Sample inquiry tabletop"],"acceptanceCriteria":["Disclosure templates live","Retention path tested","Detector-only ban in SOP"],"risks":["Disclosure without verification","Shadow AI tools","Incomplete prompt logs","Guidance misread as optional forever"]}},"retrievedAt":"2026-09-27T22:08:21-05:00","enrichedAt":"2026-09-27T22:13:16-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"RIO Boot Camp and AskORI technical assistance are the federal workforce supports named; campus training remains the local adoption layer.","procurementImplications":"Prefer research tools that export sequesterable AI-use logs; do not procure AI detectors as sole misconduct adjudication systems.","operatingModelImplications":"Disclose–verify–preserve under Part 93 is the operating model for generative AI in PHS-funded research integrity.","sourceVerification":{"openedUrl":"https://ori.hhs.gov/sites/default/files/2026-08/ORI%20Generative%20Artificial%20Intelligence%20Guidance_final.pdf","referenceExcerpt":"Researchers should disclose any generative AI tools used during research and manuscript and grant preparation","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}