{"id":"W4404848720","doi":"10.1109/access.2024.3509353","title":"Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review","year":2024,"lang":"en","type":"review","venue":"IEEE Access","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; York University; Vector Institute","funders":"","keywords":"Computer science; Narrative; Equity (law); Machine learning; Artificial intelligence; Knowledge management; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.06171763,0.00143181,0.004132162,0.01298029,0.000826345,0.005872797,0.002287421,0.002984816,0.003718121],"category_scores_gemma":[0.2629954,0.0008975865,0.004899062,0.01061863,0.002156796,0.005426331,0.002585517,0.002636007,0.0005117116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005793657,"about_ca_system_score_gemma":0.01738553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00386539,"about_ca_topic_score_gemma":0.007847973,"domain_scores_codex":[0.9489812,0.02653856,0.01431045,0.001831205,0.007860534,0.0004780316],"domain_scores_gemma":[0.6932442,0.2724506,0.0169689,0.002573819,0.01424622,0.0005163176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002561313,0.00003298297,0.001463049,0.6396332,0.006099526,0.0001210853,0.0008886462,0.0008906969,0.0001853335,0.01691263,0.008254472,0.3252623],"study_design_scores_gemma":[0.0000920673,0.0001718055,0.001485671,0.8756762,0.01246314,0.0003132914,0.0006029673,0.0004376247,0.0004094437,0.009384657,0.09889453,0.00006864371],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004198307,0.9930667,0.001745624,0.002679147,0.000335546,0.0003685339,0.0002108522,0.000009711113,0.001164166],"genre_scores_gemma":[0.01508386,0.9754801,0.005170848,0.002154138,0.0003916728,0.001287948,0.0001672652,0.00001664242,0.0002474017],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.06171763,"threshold_uncertainty_score":0.3263979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.747485209596508,"score_gpt":0.584762261388914,"score_spread":0.162722948207594,"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."}}