{"id":"W4385644241","doi":"10.1145/3600211.3604678","title":"Target specification bias, counterfactual prediction, and algorithmic fairness in healthcare","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Gottfried Wilhelm Leibniz Universität Hannover; Leibniz-Gemeinschaft; Canada Research Chairs; University of Memphis; University of Oregon; Université du Québec à Montréal","keywords":"Counterfactual thinking; Computer science; Health care; Fairness measure; Econometrics; Economics; Psychology; Throughput","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":[0.1402961,0.001244445,0.003068305,0.001848871,0.001792425,0.007246675,0.002941316,0.004040239,0.004236167],"category_scores_gemma":[0.4259936,0.001022965,0.001824187,0.002637133,0.01458289,0.008939661,0.00654613,0.00623379,0.0004520103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004277825,"about_ca_system_score_gemma":0.006051289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003538071,"about_ca_topic_score_gemma":0.001943954,"domain_scores_codex":[0.8352631,0.1397127,0.004511806,0.009092324,0.009515798,0.00190424],"domain_scores_gemma":[0.435109,0.5081815,0.01763166,0.03004971,0.007711468,0.001316616],"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":[0.0003395763,0.00007022687,0.01040967,0.0003190186,0.0003956249,0.0001318828,0.001118695,0.05076209,0.0001359672,0.8878272,0.001872053,0.0466179],"study_design_scores_gemma":[0.00004827508,0.00002984677,0.0007282584,0.00009762657,0.00003460355,0.00003246375,0.00006420948,0.04407113,0.0001789318,0.953698,0.0009933339,0.00002319927],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05766451,0.004199794,0.8959315,0.02540736,0.0005191925,0.0002559554,0.0003271603,0.0002838444,0.01541075],"genre_scores_gemma":[0.8635662,0.001489504,0.127531,0.003714839,0.0008694386,0.0006781967,0.0002611654,0.0001741873,0.001715332],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1402961,"threshold_uncertainty_score":0.7419658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3418042879734152,"score_gpt":0.4339698389670957,"score_spread":0.09216555099368051,"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."}}