{"id":"W4410447861","doi":"10.1038/s43588-025-00806-9","title":"Computational challenges arising in algorithmic fairness and health equity with generative AI","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Neurological Disorders and Stroke; Gordon and Betty Moore Foundation","keywords":"Generative grammar; Equity (law); Health equity; Computer science; Theoretical computer science; Artificial intelligence; Economics; Political science; Health care; Economic growth","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":[0.03320711,0.0005285471,0.00159392,0.0009857551,0.002371031,0.006568891,0.003211988,0.005872519,0.009544927],"category_scores_gemma":[0.1465409,0.0006983205,0.0008658828,0.0009223173,0.01665763,0.007271027,0.005613913,0.006212713,0.0004640717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003739081,"about_ca_system_score_gemma":0.004731451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00404014,"about_ca_topic_score_gemma":0.004099384,"domain_scores_codex":[0.9764901,0.01864137,0.0004810398,0.001490071,0.001990022,0.0009073975],"domain_scores_gemma":[0.8324361,0.1532777,0.002834835,0.007250409,0.002354257,0.001846721],"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":[0.00002309388,0.00002425703,0.0008475402,0.00003267741,0.00002438199,0.00004040397,0.0002429983,0.01412323,0.00004110558,0.9778906,0.0009525062,0.005757189],"study_design_scores_gemma":[0.000008579975,0.000003311182,0.0001000238,0.00001039431,0.000003859604,0.00001399014,0.0000464307,0.01490613,0.00002086003,0.9842852,0.0005974906,0.000003786729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.108792,0.003373134,0.5874379,0.1776387,0.0008886939,0.0002220256,0.0003854258,0.0001676571,0.1210944],"genre_scores_gemma":[0.9641759,0.000406641,0.02840334,0.0029409,0.000657175,0.0001486372,0.00004838859,0.00006023653,0.003158789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03320711,"threshold_uncertainty_score":0.1756181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05781304433646098,"score_gpt":0.4742315941502994,"score_spread":0.4164185498138384,"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."}}