{"id":"W4414264132","doi":"10.1002/sim.70234","title":"What Is Fair? Defining Fairness in Machine Learning for Health","year":2025,"lang":"en","type":"review","venue":"Statistics in Medicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women's and Gender Studies et Recherches Féministes; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Operationalization; Fairness measure; Capability approach; MEDLINE; Health care","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01528121,0.0009420575,0.002057652,0.002836149,0.001176403,0.004481017,0.001697619,0.004485583,0.002704568],"category_scores_gemma":[0.03153192,0.0004225026,0.0009709691,0.003095268,0.008373649,0.007166913,0.002450614,0.005661352,0.0006442708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004019656,"about_ca_system_score_gemma":0.006993595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003355232,"about_ca_topic_score_gemma":0.003035201,"domain_scores_codex":[0.9908915,0.005774614,0.0007111219,0.0007202665,0.001627847,0.0002746233],"domain_scores_gemma":[0.9726041,0.02443305,0.0009820245,0.0006212724,0.00111015,0.0002494129],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005613499,0.00004207349,0.000384612,0.01083153,0.0001915271,0.00006813108,0.0006834326,0.001852301,0.0001383752,0.5342329,0.01524547,0.4362735],"study_design_scores_gemma":[0.00002671402,0.00008028942,0.0009179494,0.02792461,0.0001458309,0.000345785,0.0005246382,0.001123596,0.0003213939,0.5847217,0.3837929,0.00007449433],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002213182,0.9818736,0.003538166,0.01043439,0.0006226545,0.00001375216,0.00002218047,0.000008113104,0.003265834],"genre_scores_gemma":[0.01897524,0.9678432,0.003607282,0.006461752,0.002263787,0.00009217853,0.00004286071,0.00001739681,0.0006962807],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9847188,"threshold_uncertainty_score":0.08081573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2301952076018682,"score_gpt":0.5545321101943922,"score_spread":0.324336902592524,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}