{"id":"W3139108984","doi":"10.3390/a14030087","title":"Local Data Debiasing for Fairness Based on Generative Adversarial Training","year":2021,"lang":"en","type":"article","venue":"Algorithms","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Debiasing; Adversarial system; Interpretability; Computer science; Machine learning; Generative grammar; Artificial intelligence; Process (computing); Data-driven","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":[0.00963337,0.001207282,0.001500466,0.0007534835,0.001034145,0.001665662,0.002397566,0.001824184,0.002823456],"category_scores_gemma":[0.02786037,0.0005804315,0.0008808135,0.0005462286,0.00414619,0.003000124,0.00523267,0.004653606,0.0005534168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001619071,"about_ca_system_score_gemma":0.001876173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001222234,"about_ca_topic_score_gemma":0.001905481,"domain_scores_codex":[0.9952887,0.002538253,0.0001774916,0.0008719094,0.0008094989,0.0003140729],"domain_scores_gemma":[0.9790874,0.01519714,0.001021912,0.003304184,0.0009063504,0.0004830772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003602087,0.0001577805,0.003621646,0.0001482636,0.0001251416,0.0002178011,0.0004898207,0.7734585,0.004296517,0.1385308,0.003731288,0.07486238],"study_design_scores_gemma":[0.0000175734,0.00003679044,0.0001596209,0.00002197735,0.000009631238,0.00004700195,0.00002207008,0.9280571,0.001262373,0.0696317,0.0007235194,0.00001064523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01662459,0.0002149684,0.979634,0.0007198771,0.00004633821,0.0000780907,0.0000466461,0.0003500263,0.002285455],"genre_scores_gemma":[0.8280748,0.0002296917,0.1658551,0.0009041071,0.000125775,0.0003091245,0.0002198994,0.0002148686,0.004066653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00963337,"threshold_uncertainty_score":0.05094677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2462270347011885,"score_gpt":0.4319499415195026,"score_spread":0.1857229068183141,"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."}}