{"id":"W7119125130","doi":"10.71781/32708","title":"La réglementation de l’intelligence artificielle dans le secteur de la conformité bancaire : approche européenne et canadienne","year":2025,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Competitive and Knowledge Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Acquiescence; Cherokee","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01017446,0.0009273809,0.0007605488,0.0029834,0.002419308,0.01208588,0.002577272,0.003824475,0.008691333],"category_scores_gemma":[0.009785211,0.0006542497,0.001739238,0.00267478,0.006011412,0.006431749,0.006000862,0.004379851,0.002174407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005967429,"about_ca_system_score_gemma":0.007034448,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03382108,"about_ca_topic_score_gemma":0.02531673,"domain_scores_codex":[0.9917285,0.002839943,0.0004165379,0.001186751,0.003172022,0.0006562903],"domain_scores_gemma":[0.9925368,0.002505991,0.0003616885,0.00188528,0.002289593,0.0004206313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002347375,0.0001451971,0.002916964,0.000351957,0.0001115995,0.0005853752,0.0116597,0.02179,0.006313925,0.7923135,0.004674297,0.1589027],"study_design_scores_gemma":[0.00008083482,0.0002391179,0.005524271,0.001353681,0.0001595674,0.0007495078,0.009180062,0.05109167,0.01034451,0.2554901,0.6655884,0.0001982659],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0717096,0.005487997,0.5008917,0.01214632,0.0005455085,0.0002525841,0.0001318989,0.001136651,0.4076979],"genre_scores_gemma":[0.6169646,0.005554846,0.2616857,0.002437048,0.0002151806,0.0004208598,0.0004364525,0.0005139305,0.1117714],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9661789,"threshold_uncertainty_score":0.0672484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02592823389972388,"score_gpt":0.2969112174884392,"score_spread":0.2709829835887154,"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."}}