{"id":"W3210632597","doi":"10.1145/3459637.3481965","title":"FairCORELS, an Open-Source Library for Learning Fair Rule Lists","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Agence Nationale de la Recherche","keywords":"Python (programming language); Computer science; Open source; Statistical learning; Space (punctuation); Upper and lower bounds; Data mining; Machine learning; Artificial intelligence; Theoretical computer science; Programming language; Mathematics; Software; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"software","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001704559,0.0002603646,0.0004815705,0.00006754123,0.001660655,0.006224331,0.001824534,0.00103919,0.001201928],"category_scores_gemma":[0.001626432,0.0002737055,0.0002149079,0.0001781108,0.0002853705,0.001970914,0.002363998,0.001435763,0.00001518832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009059809,"about_ca_system_score_gemma":0.002359081,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01565557,"about_ca_topic_score_gemma":0.006156812,"domain_scores_codex":[0.9972851,0.0006189714,0.0003256903,0.0006974507,0.0004916579,0.0005810764],"domain_scores_gemma":[0.9980484,0.0005016672,0.0002430282,0.0004041693,0.0003504336,0.0004523275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009752237,0.0008101242,0.004597784,0.0004962084,0.0003545601,0.00004209752,0.3031493,0.001139674,0.0001636022,0.5328526,0.06885653,0.08743995],"study_design_scores_gemma":[0.0004064073,0.0001780525,0.0008112479,0.0002627436,0.00005401026,4.357867e-7,0.07973132,0.0004208859,0.0001628993,0.1391585,0.7778482,0.0009653655],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1274371,0.0006260687,0.004955199,0.05176414,0.002246048,0.002269732,0.00007229462,0.001521422,0.8091081],"genre_scores_gemma":[0.8001395,0.001278337,0.02218883,0.004373163,0.00237027,0.0001423925,0.0007807317,0.0001674583,0.1685593],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7089916,"threshold_uncertainty_score":0.9999715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08882101105174957,"score_gpt":0.3995520129912106,"score_spread":0.310731001939461,"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."}}