{"id":"W3187745409","doi":"10.1177/00187208211036323","title":"Attribution Errors by People and Intelligent Machines","year":2021,"lang":"en","type":"article","venue":"Human Factors The Journal of the Human Factors and Ergonomics Society","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Attribution; Context (archaeology); Perspective (graphical); Set (abstract data type); Computer science; Comprehension; Value (mathematics); Through-the-lens metering; Work (physics); Human–computer interaction; Human error; Computer security; Data science; Artificial intelligence; Risk analysis (engineering); Psychology; Social psychology; Machine learning; Engineering; Lens (geology)","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":[0.02496544,0.0005387951,0.0003844971,0.003334453,0.002255922,0.004345061,0.000870081,0.001462581,0.002720712],"category_scores_gemma":[0.1688481,0.0003015493,0.0005095425,0.002001397,0.008887062,0.005586438,0.005723725,0.001551483,0.000270595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001696043,"about_ca_system_score_gemma":0.001825179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00232498,"about_ca_topic_score_gemma":0.001515444,"domain_scores_codex":[0.9665766,0.02136396,0.00198258,0.0015013,0.007543407,0.001032153],"domain_scores_gemma":[0.8086188,0.1096682,0.04780037,0.01808194,0.01374454,0.002085992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000695434,0.0002021588,0.5314706,0.0005054052,0.0003054831,0.0009732658,0.2637661,0.003521795,0.001289865,0.039094,0.00328553,0.1548904],"study_design_scores_gemma":[0.00006836659,0.0005084552,0.4360375,0.001805219,0.0002886814,0.002252138,0.3590287,0.01052298,0.004722231,0.1505434,0.03388261,0.0003397896],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9492914,0.001467794,0.02037927,0.004784703,0.0002859638,0.00008758769,0.00009271877,0.00009522716,0.02351529],"genre_scores_gemma":[0.9976185,0.0002091178,0.001386261,0.0001765176,0.00004739552,0.00001578857,0.00001869688,0.000009400342,0.0005182614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02496544,"threshold_uncertainty_score":0.1320314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618272074065427,"score_gpt":0.3199315633215106,"score_spread":0.2837488425808564,"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."}}