{"id":"W4280530301","doi":"10.1126/science.adc8720","title":"The bias hunter","year":2022,"lang":"en","type":"article","venue":"Science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Outrage; Political science; Law","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":[0.01126591,0.001095739,0.0009881628,0.002857537,0.004774187,0.00766082,0.001892712,0.006222238,0.0184121],"category_scores_gemma":[0.03701621,0.0006355546,0.0007614539,0.000827249,0.0119067,0.01130025,0.008949801,0.00959383,0.01159139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001888135,"about_ca_system_score_gemma":0.002424674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001125492,"about_ca_topic_score_gemma":0.001683364,"domain_scores_codex":[0.9928209,0.002198089,0.0001993155,0.001307493,0.002882566,0.0005915871],"domain_scores_gemma":[0.9802388,0.007717483,0.00110481,0.005596655,0.003679535,0.001662717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002218875,0.00006165495,0.00242067,0.0001933794,0.00008222146,0.0004859857,0.00130771,0.002178024,0.00409331,0.598243,0.1954962,0.1952159],"study_design_scores_gemma":[0.00004168778,0.0001183564,0.0007542023,0.0006260059,0.00003116684,0.002268855,0.0006966541,0.00772298,0.008200401,0.3959947,0.5834194,0.0001255791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02638394,0.01897072,0.284423,0.2646375,0.01566819,0.0002540415,0.0008061822,0.003672037,0.3851844],"genre_scores_gemma":[0.5232649,0.01071341,0.1498707,0.07147907,0.0079287,0.0003171754,0.0006693425,0.002263,0.2334938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0184121,"threshold_uncertainty_score":0.06159455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02072917719532785,"score_gpt":0.2684817712262224,"score_spread":0.2477525940308946,"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."}}