{"resourceId":"frontiers-uhm-ai-human-source-attribution-2026-09-10","versions":[{"version":"external-cd85a9acb8aa1bf528ecca6eb7292cdee722cded567923da8ca459b63b65c821","resource":{"id":"frontiers-uhm-ai-human-source-attribution-2026-09-10","title":"Frontiers experiment: AI labels raise credibility/diagnosticity; humans still win perceived athlete uniqueness","organization":"Inha University (Frontiers in Psychology)","sector":"Academic sport psychology / performance evaluation research","geography":"International (UK football-fan sample; Korean authorship; pro football stimulus)","publishedAt":"10 September 2026","publicationDate":"2026-09-10","eventDate":null,"sourceName":"Frontiers in Psychology","sourceLabel":"Peer-reviewed academic experiment","sourceUrl":"https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1954191/full","evidenceClass":"academic-research","outcomeClass":"mixed","topics":["knowledge-work","governance-procurement","operating-model","accessibility-workforce"],"finding":"Uhm et al. (published 2026-09-10) randomly assigned 220 UK football fans to identical Kevin De Bruyne performance reports labeled as AI-based or professional human analysts. AI attribution significantly increased message credibility and perceived diagnosticity, while human attribution increased perceived consideration of athlete uniqueness (partial eta-squared about 0.05-0.10). Authors stress perception is not accuracy and caution against generalizing to professional decision makers. Why now: same-week FanWord/performance-AI labeling wave makes source-disclosure design newly operational for college athletics.","sledRelevance":"Transferable caution for collegiate performance and communications teams presenting AI-assisted athlete evaluations—trust can inflate on systematic dimensions while athletes still perceive uniqueness neglect.","evidence":"Between-subjects experiment; MANCOVA/ANCOVA with familiarity covariate; adjusted means reported; sensitivity analysis including failed manipulation-check participants preserved direction.","architectureImplications":"Invest in report UX: source labels, input provenance, uncertainty, and human contextualization fields—not only model scores.","governanceImplications":"Disclosure policy and human-review statements are governance controls against overtrust; do not cite fan credibility lifts as proof of analytical quality.","securityPrivacyImplications":"Limited direct privacy findings; practical risk is oversharing sensitive athlete evaluations under an AI-credibility halo.","caveats":"Pro football stimulus and fan sample—not NCAA coaches/athletes; content held constant so no objective accuracy comparison; effect sizes moderate/dimension-specific.","streamIds":["college-athletics"],"roles":{"sales":"As athletic departments deploy AI for performance reports and storytelling, stakeholders may over-trust AI labels on systematic dimensions while still doubting individualized judgment. Discovery should ask how AI-generated athlete evaluations or recaps are labeled to coaches/athletes/fans and whether human review is disclosed. Bound value is calibrating trust—not proving AI analytical superiority. Unsupported claims include NCAA-staff generalizability (sample was UK football fans), objective accuracy gains, or that AI uniquely considers athlete context.","engineering":"Fit is presentation-layer source disclosure and explanation design for performance reports, not a new model architecture. Integration touches report templates, analyst workflows, and UI labels for AI vs human vs hybrid authorship. Prerequisites include IRB-quality care when experimenting with athlete-facing labels and clear data provenance. Constraints: identical report content was held constant; effects are perceptual; professional coaches/scouts were not sampled. Security/privacy are secondary here; avoid implying unverified model capabilities via labeling. Proof of value: A/B tests of label formats with coaches measuring reliance calibration, not vanity credibility scores alone.","delivery":"Delivery work is changing how AI-assisted evaluations are disclosed and reviewed. Dependencies include sports-science/analyst ownership and communications standards. Ownership: performance staff for content, compliance/ethics for athlete-facing claims. Skills adoption: train staff that AI labels can inflate credibility/diagnosticity perceptions. Governance checkpoints: disclosure standards, human contextualization statements, and bans on using fan-perception studies as accuracy proof. Acceptance criteria: documented label policy and a coach/athlete pilot on trust calibration. Risks: uncritical reliance on AI-attributed reports and uniqueness-neglect complaints from athletes."},"roleDetails":{"sales":{"customerProblem":"Departments adopting AI performance reports and athlete content risk miscalibrated trust when the AI label alone changes perceived credibility.","stakeholders":["Sports performance / analytics staff","Head coaches","Student-athletes","Athletics communications"],"discoveryQuestions":["How are AI-assisted evaluations currently labeled to coaches and athletes?","Is human review disclosed, and who is accountable when a report is wrong?","Which decisions (playing time, roster, rehab) may never rely on AI-only attribution?"],"valueHypothesis":"Clear hybrid disclosure and uniqueness-context fields can reduce overtrust on systematic dimensions while addressing uniqueness-neglect concerns—without claiming better accuracy.","potentialEngagement":"Bounded label/design pilot with coaches on identical reports under AI, human, and hybrid attribution; measure reliance intentions and complaint themes.","unsupportedClaims":["AI reports are objectively more accurate than human analysts","Findings generalize to NCAA coaches or scouts (fan sample)","AI systems inherently consider athlete uniqueness"]},"engineering":{"fit":"Source-attribution and explanation UX for athlete performance reports rather than a predictive model purchase.","architectureIntegration":"Report template fields for source label, data inputs, uncertainty, and human contextualization; audit log of who approved release.","prerequisites":["Defined report schema with provenance fields","Human reviewer role in workflow","Agreement on which audiences see which labels"],"constraints":["Study held content constant; effects are perceptual","UK football-fan sample, professional football stimulus","No objective accuracy benchmark against match truth"],"security":"Avoid overstating model capability in UI copy; keep sensitive athlete notes out of fan-facing labeled reports.","proofOfValue":["Coach reliance calibration tasks under alternate labels","Athlete perception of uniqueness consideration","Rate of inappropriate high-confidence decisions after AI-labeled reports"]},"delivery":{"work":"Publish a source-disclosure standard for AI-assisted performance and communications outputs; train staff; run a small coach pilot.","dependencies":["Sports performance leadership","Communications brand standards","Existing report tooling"],"ownership":"Performance analytics owns report content; athletics leadership owns disclosure policy.","skillsAdoption":"Teach staff that AI labels can raise credibility/diagnosticity perceptions without improving truth.","governanceCheckpoints":["Disclosure policy sign-off","Pilot ethics/IRB-equivalent review if athletes are research subjects","Quarterly review of mis-reliance incidents"],"acceptanceCriteria":["All AI-assisted athlete evaluations carry source and human-review fields","Documented ban on presenting fan-study perceptions as accuracy proof","Coach pilot completed with written trust-calibration findings"],"risks":["Overtrust leading to roster or rehab decisions on thin evidence","Athlete pushback on uniqueness neglect","Transferring pro-football fan findings uncritically into NCAA ops"]}},"retrievedAt":"2026-09-27T03:19:30Z","enrichedAt":"2026-09-27T03:22:00Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Staff must be trained that AI labels can raise perceived diagnosticity without improving truth—affecting how analysts present findings to coaches.","procurementImplications":"When buying performance-report or storytelling AI, require disclosure/explainability features and hybrid-review workflows in acceptance tests.","operatingModelImplications":"Supports hybrid operating models where AI drafts systematic summaries and humans explicitly own individualized athlete context.","sourceVerification":{"openedUrl":"https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1954191/full","referenceExcerpt":"AI attribution increased message credibility and perceived diagnosticity, whereas human attribution increased perceived consideration","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}