{"resourceId":"teleki-acai-due-process-audit-facct-2026","versions":[{"version":"external-02a157c2128f3a608e8a897775d571fdcfeed5b2246aac4354f3f6354587b4c0","resource":{"id":"teleki-acai-due-process-audit-facct-2026","title":"FAccT ACAI audit of 79 U.S. universities: AI governance skewed to rules, weak on feedback and detection due process","organization":"Texas A&M University / Cornell Tech / collaborators","sector":"Higher education / independent research","geography":"United States (79 universities across regions and Carnegie types)","publishedAt":"2026-06-25","publicationDate":"2026-06-25","eventDate":"2026-06-25","sourceName":"ACM FAccT 2026","sourceLabel":"Conference paper (doi:10.1145/3805689.3812328)","sourceUrl":"https://mariateleki.github.io/pdf/FAccT_26_ACAI.pdf","evidenceClass":"independent-research","outcomeClass":"cautionary","topics":["governance-procurement","data-security","accessibility-workforce","operating-model"],"finding":"Human audit of publicly legible AI governance across 79 U.S. universities finds capacity concentrated in policy clarity and faculty support, with feedback mechanisms and detection-tool safeguards underdeveloped; AI research intensity weakly correlates with ACAI governance scores; LLM audits misrank institutions. Why now: newly relevant after the 2026-09-18/19 Student Defense SHAPE coverage that adoption outpaces student protections—this paper quantifies procedural due-process gaps SHAPE warns about.","sledRelevance":"Student-success AI advising, tutoring, and integrity enforcement all depend on contestable procedures. U.S. public colleges face Fourteenth Amendment and published-procedure risks when detectors drive discipline without human review and recourse.","evidence":"ACAI-US79 dataset; three Prolific annotators per institution; domains A–D; comparison to CSRankings; LLM replication study showing unstable rankings. Public materials only (lower bound). Link collection Dec 8–30, 2025.","architectureImplications":"Central public AI governance hub; integrity case systems must treat detector scores as advisory with mandatory human review gates.","governanceImplications":"Stand up participatory feedback committees, publish update logs, and specify appeal paths for AI-related academic determinations.","securityPrivacyImplications":"Detection workflows process student writing; govern vendors, false-positive risk for multilingual writers, and access to misconduct case data.","caveats":"Public-legibility lower bound; U.S.-centric; purposive sample; does not measure internal intranet policies or learning outcomes.","streamIds":["student-success"],"roles":{"sales":"ACAI-US79 audits 79 diverse U.S. universities and finds AI governance capacity skewed to policy clarity and faculty support, with weak feedback and detection-tool due-process safeguards. Research intensity poorly predicts accountability. Newly relevant after recent student-protection warnings: campuses need auditable recourse around AI detection and participatory revision, not more honor-code text alone. Offer a governance gap assessment mapped to ACAI domains—not a product that automates misconduct findings.","engineering":"Fit is a governance documentation and workflow layer: public policy hubs, syllabus templates, committee charters, and detection-tool procedures requiring human review. Integrate with academic integrity case systems so detector outputs are advisory evidence only. Prerequisites: inventory of authoritative URLs and owners. Constraints: ACAI measures public legibility (lower bound); intranet policies invisible. Security/privacy: student misconduct records remain highly sensitive; do not let detectors auto-decide. Proof: raise public scores on feedback and detection domains in a re-audit.","delivery":"Run a time-bounded ACAI-style self-audit, publish a central AI governance page, stand up student/faculty feedback channels, and write detection-tool SOPs with human review and appeal paths. Ownership: provost office with integrity, teaching center, and counsel. Skills: policy drafting and stakeholder facilitation. Governance checkpoints: committee charters and public update logs. Acceptance: documented human-review rule and recourse path; risks include performative policy without resources and LLM-only audits that misrank capacity."},"roleDetails":{"sales":{"customerProblem":"AI honor codes and detectors expanded faster than contestable due-process infrastructure students and faculty can find and use.","stakeholders":["provost / general counsel","academic integrity office","faculty senate","student government","teaching & learning"],"discoveryQuestions":["Can a student locate who reviews AI-detection flags and how to appeal within five minutes?","Is there a standing student/faculty AI governance committee?","Are detectors advisory or determinative?"],"valueHypothesis":"Improving public procedural legibility reduces inequitable informal guidance and legal/reputational risk around AI misconduct cases.","potentialEngagement":"ACAI-aligned governance readiness assessment and remediation roadmap for feedback and detection domains.","unsupportedClaims":["That high ACAI score equals ethical AI use","That detectors become accurate via policy","Automated LLM audit replacing human review"]},"engineering":{"fit":"Policy CMS + integrity case workflow with mandatory human review gates on AI-detection evidence.","architectureIntegration":"Link central policy hub, syllabus statement library, committee pages, and case-management system; detectors emit advisory scores only.","prerequisites":["Authoritative content owners","Counsel-approved appeal language","Public vs authenticated content map"],"constraints":["Public-audit lower bound","U.S. legal framing","Purposive 79-university sample"],"security":"Misconduct case data needs strict access control; avoid exposing detector vendor models that process student work without DPA.","proofOfValue":["Before/after ACAI domain scores","Appeal pathway usability test","Zero sole-detector determinations in sample term"]},"delivery":{"work":"Inventory policies, run multi-annotator ACAI checklist, draft detection SOP, launch feedback committee, publish update log.","dependencies":["Counsel","integrity office","web/comms","faculty/student reps"],"ownership":"Provost-designated AI governance lead.","skillsAdoption":"Train instructors that detector scores are investigative leads requiring human judgment.","governanceCheckpoints":["Public posting of SOP","Committee first meeting minutes","Annual re-audit"],"acceptanceCriteria":["Human-review requirement published","Student feedback channel live","Detection tool stance documented"],"risks":["Shadow AI continues","Resource-thin committees","False confidence from policy text alone"]}},"retrievedAt":"2026-09-21T03:05:40Z","enrichedAt":"2026-09-21T03:18:00Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Opaque detector governance disproportionately risks multilingual and less-connected students who rely on published rules rather than informal faculty networks.","procurementImplications":"Procurement of detection or student-AI tools should require human-review clauses, appeal documentation, and public policy artifacts—not accuracy marketing alone.","operatingModelImplications":"Shift from honor-code updates alone to standing shared-governance structures that revise AI policy with student and faculty input.","sourceVerification":{"responseId":"web-fetch-facct-acai-pdf","openedUrl":"https://mariateleki.github.io/pdf/FAccT_26_ACAI.pdf","referenceExcerpt":"governance capacity is systematically concentrated in rule articulation while mechanisms for participation, feedback, and procedural constraint are underdeveloped","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}