{"id":"W4220737446","doi":"10.1007/s43681-022-00149-5","title":"Artificial intelligence and consumer manipulations: from consumer's counter algorithms to firm's self-regulation tools","year":2022,"lang":"en","type":"article","venue":"AI and Ethics","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Center for Interuniversity Research and Analysis on Organizations; Polytechnique Montréal","funders":"","keywords":"Transparency (behavior); Compliance (psychology); Computer science; Consumer protection; Consumer choice; Risk analysis (engineering); Economics; Marketing; Business; Computer security; Internet privacy; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0006863401,0.00006884192,0.00008720533,0.00004470982,0.0009934509,0.000279478,0.00006876663,0.00008814474,0.0002713737],"category_scores_gemma":[0.00007678279,0.00007779575,0.00001814302,0.0001338096,0.0001368088,0.0005778294,0.00003892943,0.0002596915,0.00003005346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004386863,"about_ca_system_score_gemma":0.0000975229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00103428,"about_ca_topic_score_gemma":0.00128477,"domain_scores_codex":[0.999176,0.0001127844,0.0001990286,0.0001616757,0.0002125045,0.0001379992],"domain_scores_gemma":[0.9993175,0.0004318456,0.00004009266,0.00006756194,0.00006190108,0.00008107673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005199401,0.00008798471,0.005422119,0.00002044713,0.00006458092,0.000002459374,0.1182161,0.0005743396,0.00000936653,0.3183512,0.001193717,0.5560057],"study_design_scores_gemma":[0.0001430607,0.0001129898,0.008384977,0.00004008709,0.00006579505,0.000003378623,0.01577409,0.01064149,0.00009913828,0.2076851,0.7565756,0.0004742736],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7678781,0.001766115,0.09492276,0.104403,0.002008226,0.002003958,0.0006222287,0.0004186782,0.02597697],"genre_scores_gemma":[0.9961923,0.000198126,0.0005744062,0.002788402,0.000063406,0.00002986776,0.00005765385,0.000004823962,0.00009105884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7553819,"threshold_uncertainty_score":0.7640921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09169598200785962,"score_gpt":0.3323981445893953,"score_spread":0.2407021625815357,"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."}}