{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002480277,0.0001288267,0.0002047762,0.0001165879,0.00005189007,0.00006491896,0.000214488,0.00008681956,0.0004257939],"category_scores_gemma":[0.0003114382,0.0001009519,0.00007124078,0.0006730241,0.0001311904,0.0001210151,0.00005172174,0.0002458064,0.00005258694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003279813,"about_ca_system_score_gemma":0.0001912246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002223262,"about_ca_topic_score_gemma":0.00003860403,"domain_scores_codex":[0.9987305,0.00001160491,0.0003620963,0.0002704094,0.0004265434,0.0001987859],"domain_scores_gemma":[0.99913,0.00002326071,0.0001289034,0.0001077178,0.000511323,0.00009881193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005463878,0.001669604,0.04605412,0.001065737,0.00007318238,0.00002161802,0.009239903,0.000003330122,0.1110596,0.5574829,0.0002466269,0.2725371],"study_design_scores_gemma":[0.0001822528,0.0001979581,0.01588026,0.001222111,0.00006057338,0.00003159981,0.002172708,0.0007459312,0.9586781,0.02031719,0.0003393216,0.0001720211],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9613332,0.00003996849,0.0002829647,0.01153712,0.0003385011,0.0006661694,0.00001750551,0.00006731365,0.0257173],"genre_scores_gemma":[0.9983363,0.00007142404,0.0003422008,0.0005800078,0.00005494359,0.00002665173,0.00001536045,0.00001201657,0.0005611101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8476185,"threshold_uncertainty_score":0.4662145,"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."}}