{"id":"W4283162852","doi":"10.1002/jcpy.1313","title":"How to overcome algorithm aversion: Learning from mistakes","year":2022,"lang":"en","type":"article","venue":"Journal of Consumer Psychology","topic":"Psychology of Moral and Emotional Judgment","field":"Neuroscience","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto; HEC Montréal","funders":"","keywords":"Computer science; Variety (cybernetics); Mediation; Moderation; Advice (programming); Loss aversion; Product (mathematics); Algorithm; Process (computing); Psychology; Artificial intelligence; Economics; Machine learning; Microeconomics; Sociology; Mathematics","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":[0.009978222,0.0004144769,0.0003583801,0.0005623686,0.0006740976,0.002824941,0.0008988566,0.00177909,0.00549911],"category_scores_gemma":[0.07253668,0.0003591645,0.0003498793,0.0002937014,0.003543778,0.003425848,0.001501396,0.003240103,0.0007772223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007561193,"about_ca_system_score_gemma":0.001293967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001156681,"about_ca_topic_score_gemma":0.001225464,"domain_scores_codex":[0.9950809,0.002867171,0.0001873165,0.0004846925,0.001063027,0.00031686],"domain_scores_gemma":[0.9417232,0.03728106,0.008918436,0.006928074,0.003653616,0.001495661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002090181,0.00416914,0.3369442,0.001850329,0.0008546597,0.001106619,0.03459112,0.01356952,0.04393295,0.1149964,0.01036428,0.4355306],"study_design_scores_gemma":[0.0007176602,0.005651902,0.2526311,0.001831639,0.001272628,0.001846569,0.02785814,0.1156896,0.06068121,0.4614918,0.06984231,0.000485383],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9351427,0.0003570727,0.03845934,0.00660888,0.00008374908,0.00007637959,0.00003008834,0.0002089456,0.01903284],"genre_scores_gemma":[0.9905515,0.0001212862,0.007296125,0.0009504817,0.00001698591,0.00002311765,0.00001704186,0.00002594618,0.000997615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009978222,"threshold_uncertainty_score":0.0527705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1046464773891632,"score_gpt":0.3053059748613368,"score_spread":0.2006594974721736,"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."}}