{"resourceId":"mdpi-ethical-bias-ai-injury-prediction-2025","versions":[{"version":"external-59f2e8a86437b7dcd8280d83e91a86a5450dcadac45b4eb178ea272468f596f2","resource":{"id":"mdpi-ethical-bias-ai-injury-prediction-2025","title":"MDPI review: AI injury-prediction ethics lag safeguards across collegiate and elite sport","organization":"MDPI / Academy of Physical Education Katowice et al.","sector":"Academic sports science / AI ethics","geography":"International (collegiate contexts included)","publishedAt":"November 1, 2025","publicationDate":"2025-11-01","eventDate":null,"sourceName":"MDPI AI","sourceLabel":"Peer-reviewed narrative review","sourceUrl":"https://www.mdpi.com/2673-2688/6/11/283","evidenceClass":"academic-research","outcomeClass":"cautionary","topics":["data-security","governance-procurement","accessibility-workforce","operating-model"],"finding":"Narrative review of 24 studies on AI-driven injury forecasting finds recurring gaps in privacy, fairness, informed consent, athlete autonomy, and long-term data governance despite reported model performance tools (e.g., SHAP). HIPAA, FERPA, and GDPR are judged only partial and inconsistently applied; collegiate settings appear among under-protected contexts. Authors recommend enforceable athlete data rights, participatory oversight, explainability with contestability, and independent audits. Newly relevant now as college S&C assistants (e.g., FYTT Flex) and performance-tech stacks expand AI into programming and risk workflows without published ethics audits.","sledRelevance":"Independent academic caution for collegiate athletic departments deploying injury-prediction or load AI—constrains how Flex-class and wearable stacks should be governed.","evidence":"PRISMA-guided narrative synthesis of 24 studies; thematic findings and recommendation tables; explicit collegiate citations in governance discussion.","architectureImplications":"Favor explainable models, human-in-the-loop gates, federated or minimized centralization where possible, and athlete-facing contestability interfaces—not opaque vendor scores alone.","governanceImplications":"Institutions should codify data rights, ethics boards with athlete voice, and appeal paths before predictive labels affect playing time, scholarships, or medical decisions.","securityPrivacyImplications":"Biometric and health-adjacent streams need dynamic consent, retention limits, and limits on secondary commercial use; FERPA/HIPAA alone are insufficient design specs.","caveats":"Narrative review (search ~Jan 2025); not a new college RCT; heterogeneity limits quantitative pooling; Poland-based authorship synthesizing global literature.","streamIds":["college-athletics"],"roles":{"sales":"Athletic directors buying injury or load AI face ethics and liability exposure, not just accuracy pitches. Discovery should ask who owns biometric data, whether student-athletes can contest risk labels, and how predictions may influence playing time, scholarships, or NIL talks. Bound value is a governance-first conversation that surfaces rights gaps before opaque vendor models lock in—not selling fear. Use the review’s five concern axes as a checklist with sports medicine and privacy counsel. Unsupported: declaring any vendor unsafe from this paper alone, guaranteeing compliance by reading it, or copying pro-league CBA norms into NCAA settings without adaptation.","engineering":"Fit is a cautionary requirements stack for injury-prediction and load-AI systems in collegiate environments rather than a product SKU. Architecture should wire human-in-the-loop medical override, explainability outputs, access controls separating coaching from medical uses, and optional minimized centralization patterns. Prerequisites include lawful bases for data, bias testing across sex, age, and ability subgroups, and documented injury-definition standards. Constraints include partial FERPA and HIPAA coverage, vendor secrecy that limits auditability, and small collegiate samples. Security requires minimized biometric retention and bans on undisclosed secondary AI-training use. Proof of value is a bias audit, appeal-path tabletop, and physician calibration review—not AUC alone.","delivery":"Stand up governance before go-live: consent scripts, sports-medicine authority, and quarterly fairness reviews with athlete representation where feasible. Dependencies include university privacy and legal offices plus vendor transparency. Ownership splits clinical use to sports medicine, data rights to the privacy office, and policy sponsorship to the athletic director. Train coaches that AI flags inform rather than replace medical decisions and educate athletes on rights and appeal. Acceptance: a live written appeal process, no automatic roster exclusion from an AI score alone, and contract clauses for deletion and training-use limits. Risks include automation bias and labels leaking into scholarship talks."},"roleDetails":{"sales":{"customerProblem":"Departments adopting injury/load AI lack clear athlete-data rights and contestability, creating compliance and trust risk alongside performance promises.","stakeholders":["Sports medicine / team physicians","Compliance / privacy office","Student-athlete representatives","Athletics administration"],"discoveryQuestions":["Can a student-athlete see and appeal an AI risk label?","Which laws (FERPA/HIPAA/state biometric) does counsel map to wearable vendors?","Do predictions influence scholarships, playing time, or NIL without human review?"],"valueHypothesis":"Governance-first discovery reduces deployment risk by surfacing rights gaps before contracts lock in opaque vendor models.","potentialEngagement":"Ethics-and-controls assessment workshop mapping one proposed injury model against the review’s five concern axes before procurement.","unsupportedClaims":["That this review proves any specific vendor is unsafe","Guaranteed legal compliance from reading the paper alone","Transfer of pro-league CBA norms wholesale to NCAA without adaptation"]},"engineering":{"fit":"Cautionary requirements stack for injury-prediction and load-AI systems in collegiate environments—not a product SKU.","architectureIntegration":"Human-in-the-loop medical override; explainability outputs; access controls separating coaching vs medical uses; optional federated learning patterns.","prerequisites":["IRB/ethics review when research-like","Representativeness checks for women/youth/disability subgroups","Documented injury definition standards"],"constraints":["Partial FERPA/HIPAA coverage","Vendor secrecy limiting auditability","Small collegiate sample sizes"],"security":"Minimize biometric retention; ban undisclosed secondary AI training use; encrypt and role-limit health-adjacent features.","proofOfValue":["Bias audit report before production","Appeal-path tabletop exercise","Calibration and false-flag review with team physicians"]},"delivery":{"work":"Policy drafting, consent UX, ethics board standup, vendor questionnaire aligned to privacy/fairness/autonomy/governance axes.","dependencies":["University privacy/legal","Faculty athletics representative","Vendor transparency"],"ownership":"Sports medicine leads clinical use; privacy office leads data rights; AD sponsors policy.","skillsAdoption":"Train coaches that AI flags inform—not replace—medical decisions; educate athletes on rights and appeal.","governanceCheckpoints":["Pre-procurement ethics review","Go-live consent audit","Semester fairness/privacy retrospective"],"acceptanceCriteria":["Written athlete appeal process live","No automatic roster exclusion from AI score alone","Vendor contract includes data-deletion and training-use limits"],"risks":["Automation bias benching healthy athletes","Predictive labels leaking into NIL/scholarship talks","Paper’s international lenses mis-applied without U.S. counsel"]}},"retrievedAt":"2026-09-25T03:17:50Z","enrichedAt":"2026-09-25T03:19:30Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Calls out underrepresentation of women, youth, and Paralympic athletes in training data—equity risk for collegiate multi-sport departments.","procurementImplications":"Use review axes as RFP/security questionnaire requirements for injury and wearable AI vendors serving NCAA clients.","operatingModelImplications":"Pushes operating model from coach-only dashboards toward shared athlete-medicine-privacy governance with contestability.","sourceVerification":{"openedUrl":"https://www.mdpi.com/2673-2688/6/11/283","referenceExcerpt":"This narrative review critically examines 24 empirical and conceptual studies focused on AI-driven injury forecasting systems.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}