{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003387014,0.0001325094,0.0002751666,0.0002391485,0.0002882281,0.00003415276,0.0005125413,0.00005454537,0.001303692],"category_scores_gemma":[0.0001817779,0.0001218098,0.0001448334,0.000233543,0.0001119746,0.0001322074,0.00008232422,0.0007549645,0.0001127412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006192672,"about_ca_system_score_gemma":0.0000356432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005885402,"about_ca_topic_score_gemma":4.934976e-7,"domain_scores_codex":[0.9981623,0.0004593826,0.0003264951,0.0003100985,0.000471015,0.0002707603],"domain_scores_gemma":[0.9989954,0.0002562489,0.000325547,0.0001768741,0.00005798914,0.0001879216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001819386,0.001390502,0.008588507,0.000007250135,0.0001958555,0.003081311,0.002004071,0.0008736189,0.5141099,0.0005202112,0.2961797,0.1712297],"study_design_scores_gemma":[0.001887616,0.001138298,0.01466849,0.00001140242,0.00002941848,0.002004928,0.0004354823,0.00004614402,0.003312233,0.002301912,0.9739532,0.0002108012],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9367875,0.0006791414,0.004654269,0.04966125,0.005511978,0.0001180373,0.00003813197,0.00002939181,0.002520248],"genre_scores_gemma":[0.9770763,0.00009534627,0.002957618,0.01694262,0.000310576,0.000006387079,0.000002668109,0.00001715785,0.002591327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6777736,"threshold_uncertainty_score":0.9996092,"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."}}