{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001951575,0.0003895809,0.002565285,0.0009766874,0.000111525,0.00002353925,0.0001627972,0.0003116409,0.0001845154],"category_scores_gemma":[0.003607586,0.0003244327,0.00009827784,0.0009164236,0.0001231206,0.00007060622,0.0000413574,0.001252695,0.00002286408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007675787,"about_ca_system_score_gemma":0.002239659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002205463,"about_ca_topic_score_gemma":0.001616879,"domain_scores_codex":[0.9962965,0.0002655567,0.001942483,0.000582032,0.0003412569,0.0005721323],"domain_scores_gemma":[0.9954285,0.003345953,0.0005046534,0.0003376634,0.0001964212,0.0001868073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002865668,0.00005379926,0.0002783065,0.06787331,0.00002337842,0.00002122489,0.002638103,0.000003865792,1.109036e-8,0.00251278,0.005461816,0.9211047],"study_design_scores_gemma":[0.0002193651,0.0006359086,0.00002064248,0.1720939,0.0002449768,0.00002090537,0.002843611,0.0009105647,3.291081e-7,0.005244465,0.8175284,0.0002369104],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001149185,0.9684212,0.02264811,0.004354864,0.002214479,0.002008893,0.0001215436,0.00002999428,0.0001997593],"genre_scores_gemma":[0.00003695512,0.9882664,0.004908393,0.001870995,0.0003766814,0.0003759711,0.002585095,0.00005473698,0.001524786],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9208679,"threshold_uncertainty_score":0.9999208,"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."}}