{"id":"W4399695830","doi":"10.48550/arxiv.2406.09307","title":"What is Fair? Defining Fairness in Machine Learning for Health","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Health care; Computer science; Human–computer interaction; Artificial intelligence; Knowledge management; Psychology; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.06513001,0.0006700222,0.001230541,0.002336803,0.005053025,0.009827939,0.001899233,0.005342726,0.002935771],"category_scores_gemma":[0.1659611,0.0004512074,0.0008205065,0.002239382,0.03632031,0.01206089,0.006184989,0.0067165,0.00036537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005789796,"about_ca_system_score_gemma":0.0075982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004171165,"about_ca_topic_score_gemma":0.002635731,"domain_scores_codex":[0.9177621,0.06476542,0.002705177,0.004623908,0.008123696,0.002019665],"domain_scores_gemma":[0.8634514,0.1125059,0.006836037,0.008900398,0.006069052,0.002237207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001725852,0.00001304373,0.001000143,0.00006208172,0.00002164161,0.00002240986,0.001025751,0.001609663,0.00004283367,0.9859788,0.001115008,0.00909132],"study_design_scores_gemma":[0.000003952351,0.000008656851,0.0002673053,0.0001010602,0.000006115225,0.00001573723,0.0001663782,0.001562895,0.0000505066,0.9951295,0.002678653,0.000009168036],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06709201,0.02522034,0.5608212,0.21174,0.002696571,0.0002881744,0.000443285,0.0001403599,0.1315581],"genre_scores_gemma":[0.9450324,0.003405833,0.03867035,0.008436994,0.001745776,0.0003484949,0.00007374096,0.00008314895,0.002203305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06513001,"threshold_uncertainty_score":0.3444445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1384651088797652,"score_gpt":0.3074682832699217,"score_spread":0.1690031743901566,"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."}}