{"id":"W6949947903","doi":"10.5281/zenodo.7255933","title":"Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?","year":2022,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Concordia University","funders":"","keywords":"Lead (geology); Ensemble learning; Set (abstract data type); Key (lock); Boosting (machine learning); Statistical classification","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":[0.02456737,0.001080365,0.001387409,0.0009812966,0.001385761,0.0022496,0.001128706,0.001596056,0.002899137],"category_scores_gemma":[0.07689941,0.0003408809,0.0008157075,0.0007816122,0.00126723,0.003577545,0.001173736,0.002687992,0.0007236273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001220055,"about_ca_system_score_gemma":0.002744918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003145593,"about_ca_topic_score_gemma":0.004685764,"domain_scores_codex":[0.9928368,0.004336042,0.0002933243,0.001244401,0.0009540993,0.0003353949],"domain_scores_gemma":[0.9472243,0.03774372,0.001440518,0.006436537,0.006213469,0.0009415317],"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":[0.002892567,0.0007821768,0.03508229,0.0005168781,0.0008333096,0.0001515441,0.0009137342,0.2380338,0.01108211,0.06849745,0.01382219,0.6273919],"study_design_scores_gemma":[0.0001392624,0.0007027438,0.009795972,0.0001475609,0.0001846613,0.000174937,0.0001879944,0.9000704,0.01556851,0.06775893,0.00517745,0.00009155583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1319299,0.002278984,0.8558475,0.002191016,0.0007787382,0.0001593669,0.0001495223,0.001137924,0.005527054],"genre_scores_gemma":[0.7100646,0.0005152734,0.2842181,0.000691117,0.0003967215,0.0002013217,0.0002783907,0.0002693823,0.003364907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02456737,"threshold_uncertainty_score":0.1299262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02278031757251286,"score_gpt":0.3003024008580591,"score_spread":0.2775220832855462,"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."}}