{"id":"W3175312069","doi":"10.1609/aaai.v35i13.17369","title":"Individual Fairness in Kidney Exchange Programs","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Organ Donation and Transplantation","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université de Montréal; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Institut de Valorisation des Données; Compute Canada","keywords":"Solver; Computer science; Mathematical optimization; Kidney transplant; Selection (genetic algorithm); Vulnerability (computing); Process (computing); Constraint (computer-aided design); Kidney transplantation; Medicine; Mathematics; Transplantation; Machine learning; Surgery; Computer security","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":[0.01199665,0.0006729079,0.001313773,0.0007649186,0.001353819,0.002659477,0.001387178,0.001264856,0.003149516],"category_scores_gemma":[0.03113488,0.0004918379,0.0008651418,0.001029082,0.001964765,0.002546369,0.00220058,0.002002145,0.0001797126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002568309,"about_ca_system_score_gemma":0.004972212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005924275,"about_ca_topic_score_gemma":0.005632663,"domain_scores_codex":[0.9915155,0.00546977,0.00025237,0.0007837337,0.001102695,0.0008760015],"domain_scores_gemma":[0.9738145,0.02211452,0.001429211,0.0009161912,0.001028941,0.0006966779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002662002,0.0001374888,0.002656941,0.0001217005,0.00003949697,0.0000833374,0.0003125564,0.8431174,0.0006601654,0.108134,0.0007487095,0.04372212],"study_design_scores_gemma":[0.0000618807,0.0001248856,0.0004497576,0.00004066018,0.00002524027,0.000033219,0.0001789058,0.8652049,0.001324722,0.1307458,0.001791551,0.00001854013],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1298121,0.0005673105,0.8576769,0.001441153,0.00005794648,0.0003082964,0.0001617894,0.000277448,0.009697073],"genre_scores_gemma":[0.7495236,0.0002848787,0.247123,0.0001846963,0.00003655461,0.0002838685,0.0000981373,0.0001052469,0.002360006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01199665,"threshold_uncertainty_score":0.06344509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1025052322303667,"score_gpt":0.3181132891456833,"score_spread":0.2156080569153166,"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."}}