{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002415079,0.00005293362,0.0000415685,0.00006903371,0.003414104,0.0003368219,0.003954706,0.000005111739,0.00003621967],"category_scores_gemma":[0.000341947,0.00003698106,0.00002502486,0.00140237,0.0003536712,0.000488603,0.002653008,0.0002422388,0.00004287102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009000839,"about_ca_system_score_gemma":0.0002066182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009775993,"about_ca_topic_score_gemma":0.00000192698,"domain_scores_codex":[0.9984051,0.0001011963,0.00009505357,0.0003085373,0.0007748871,0.0003152011],"domain_scores_gemma":[0.9990651,0.0001756718,0.00006010724,0.0006089067,0.00003959703,0.00005059138],"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":[0.000006489943,0.0000330578,0.006014198,0.000001658093,0.000004101953,0.00002876621,0.003213311,0.07433906,0.002266558,0.7210234,0.001971063,0.1910984],"study_design_scores_gemma":[0.000181285,0.00009532321,0.0117598,0.000002252373,0.000001462295,0.0000597164,0.0005024614,0.5911218,0.0006090041,0.01536543,0.38007,0.0002314126],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06392052,0.0002772865,0.8792718,0.01651604,0.007023232,0.0002551994,0.000001004648,0.0004520187,0.03228291],"genre_scores_gemma":[0.9897357,0.000001507768,0.008374901,0.0005682976,0.00004172626,0.00001610669,7.293088e-8,0.000003058874,0.00125865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9258152,"threshold_uncertainty_score":0.9978833,"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."}}