{"id":"W2988821602","doi":"10.2478/nimmir-2019-0012","title":"Let the Machine Decide: When Consumers Trust or Distrust Algorithms","year":2019,"lang":"en","type":"article","venue":"NIM Marketing Intelligence Review","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Distrust; Computer science; Feeling; Task (project management); Field (mathematics); Algorithm; Machine learning; Cognition; Ambivalence; Artificial intelligence; Human intelligence; Psychology; Social psychology; Management","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":[0.008382565,0.0002079455,0.0003757266,0.000720597,0.001012652,0.004487345,0.0006387228,0.003252171,0.004157691],"category_scores_gemma":[0.03914856,0.0002471372,0.0003228622,0.000651461,0.003617659,0.004399123,0.00118348,0.003328704,0.0006970505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002421098,"about_ca_system_score_gemma":0.0012117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004173703,"about_ca_topic_score_gemma":0.004752208,"domain_scores_codex":[0.992507,0.005272401,0.0001829853,0.0002830006,0.001370491,0.0003840759],"domain_scores_gemma":[0.9693154,0.02253718,0.002605905,0.0009106989,0.004127071,0.0005038558],"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":[0.0016666,0.0003132358,0.03734363,0.002852621,0.000495962,0.001533138,0.02843422,0.0008895184,0.003307908,0.2620831,0.1070844,0.5539956],"study_design_scores_gemma":[0.0002888074,0.0006681589,0.06014737,0.00723399,0.0005388043,0.001871849,0.04163157,0.005543278,0.004370965,0.2374281,0.6400862,0.0001908731],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3463998,0.1520439,0.01624539,0.3120236,0.001630923,0.0001715522,0.0001983916,0.000158562,0.1711278],"genre_scores_gemma":[0.9284794,0.02731483,0.001657762,0.03605281,0.0006999662,0.0000570408,0.00008191112,0.00005045878,0.005605746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008382565,"threshold_uncertainty_score":0.04433173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06717547046298424,"score_gpt":0.3935460101514361,"score_spread":0.3263705396884519,"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."}}