{"id":"W3045518291","doi":"10.48550/arxiv.2007.14361","title":"Assessing Risks of Biases in Cognitive Decision Support Systems","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Probabilistic logic; Computer science; Component (thermodynamics); Biometrics; Cognition; Process (computing); Decision support system; Risk analysis (engineering); Cognitive bias; Face (sociological concept); Risk assessment; Machine learning; Artificial intelligence; Computer security; Psychology; Business","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":[0.02079163,0.001030809,0.0009711162,0.001511478,0.001147449,0.004339062,0.001150742,0.001722118,0.001256747],"category_scores_gemma":[0.1006743,0.000526074,0.0007776483,0.001139092,0.002090797,0.005634749,0.003586743,0.002641736,0.0001795345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001916318,"about_ca_system_score_gemma":0.001535424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003369393,"about_ca_topic_score_gemma":0.001869395,"domain_scores_codex":[0.9880207,0.006240434,0.0007096347,0.001009924,0.003285818,0.0007334503],"domain_scores_gemma":[0.9110395,0.07297616,0.006542169,0.00415639,0.004231695,0.001054013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001239513,0.0005684646,0.04825586,0.0004094409,0.000597729,0.0003319074,0.002763501,0.6877298,0.005009344,0.07683635,0.001014082,0.1752441],"study_design_scores_gemma":[0.00005865382,0.0004889546,0.008938221,0.0001251329,0.0001180479,0.0001243765,0.0007720925,0.8084223,0.003702767,0.1760057,0.001132408,0.0001114717],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6670275,0.0007398856,0.3230446,0.002024531,0.00007951329,0.0001980256,0.0001089732,0.0002555069,0.006521458],"genre_scores_gemma":[0.9613048,0.0001764993,0.03802831,0.00009952601,0.00002809341,0.00006757723,0.00003067472,0.00001764215,0.0002470114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02079163,"threshold_uncertainty_score":0.109958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6998893331342111,"score_gpt":0.4081550050253572,"score_spread":0.2917343281088539,"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."}}