{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001325032,0.000100732,0.0001787282,0.00002876595,0.001014737,0.0002494993,0.0003349714,0.0001811106,0.00007143395],"category_scores_gemma":[0.00215516,0.0001051333,0.00006861451,0.0001970612,0.0002712466,0.0003680566,0.00006060763,0.0001987079,0.000006262321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009948191,"about_ca_system_score_gemma":0.001416603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001022712,"about_ca_topic_score_gemma":0.002796624,"domain_scores_codex":[0.9985014,0.0002259551,0.0001452443,0.00034366,0.0004153234,0.0003684048],"domain_scores_gemma":[0.9983351,0.0008540581,0.00005674117,0.0002791786,0.0003250642,0.0001498956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000112541,0.0003259314,0.00005891547,0.00002887223,0.0001598056,0.000164228,0.1091356,0.002067611,0.0002531309,0.1270293,0.008911622,0.7517524],"study_design_scores_gemma":[0.004367138,0.000408744,0.0002600691,0.0002106694,0.0001743786,0.00000301407,0.1495667,0.4711865,0.002560781,0.06009308,0.3099696,0.001199358],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002690891,0.0001256111,0.9134175,0.0400162,0.003169671,0.0004981424,0.0003781759,0.0001378561,0.03956594],"genre_scores_gemma":[0.9440385,0.00003247537,0.04738087,0.004813829,0.002643261,0.00001488458,0.00030852,0.00002903818,0.0007386045],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9413476,"threshold_uncertainty_score":0.7804637,"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."}}