{"id":"W3165211483","doi":"10.1145/3468507.3468511","title":"On the Applicability of Machine Learning Fairness Notions","year":2021,"lang":"en","type":"article","venue":"ACM SIGKDD Explorations Newsletter","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Agence Nationale de la Recherche","keywords":"Subjectivity; Set (abstract data type); Computer science; Artificial intelligence; Machine learning; Fairness measure; Epistemology","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":[0.04046299,0.001112392,0.001358533,0.003191134,0.003987904,0.006649208,0.002376873,0.003349282,0.004439565],"category_scores_gemma":[0.1189348,0.0005449083,0.001596481,0.002520363,0.01391042,0.01280024,0.005647481,0.007344269,0.0005563529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004570568,"about_ca_system_score_gemma":0.003215683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002863401,"about_ca_topic_score_gemma":0.001292878,"domain_scores_codex":[0.9651904,0.02075117,0.001511089,0.004062342,0.006583966,0.001901029],"domain_scores_gemma":[0.8317467,0.1450293,0.006539567,0.008170796,0.006534627,0.001979101],"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":[0.00007646711,0.00004104744,0.001500467,0.00007125486,0.00002010748,0.00005189357,0.0003968925,0.01139375,0.000167106,0.9676468,0.0009345767,0.01769955],"study_design_scores_gemma":[0.00001616356,0.00004706016,0.0006826743,0.0001177595,0.00001196109,0.00006520068,0.0001595122,0.03639375,0.0002778718,0.9586048,0.003597314,0.0000258814],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06663588,0.006639157,0.8209251,0.02175014,0.000777714,0.000239966,0.0002746684,0.0002235898,0.08253382],"genre_scores_gemma":[0.8743927,0.00239099,0.1151718,0.002527869,0.001320004,0.0003399654,0.0001048635,0.0001249119,0.003626858],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04046299,"threshold_uncertainty_score":0.2139913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08874008202605167,"score_gpt":0.3517598061059928,"score_spread":0.2630197240799412,"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."}}