{"id":"W4403331945","doi":"10.9785/cri-2024-250501","title":"The Canadian Artificial Intelligence and Data Act and the EU AI Act: Will Sanity Prevail as they more closely align? – Part 2 — Changes to both Acts bring them closer together... but not too close","year":2024,"lang":"en","type":"article","venue":"Computer Law Review International","topic":"Legal and Policy Analysis in Indonesia","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sanity; Law; Political science; Psychology; Business; Law and economics; Sociology","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":[0.02122758,0.0006673241,0.0008743577,0.005429785,0.01106123,0.01885763,0.003495623,0.01288166,0.007999444],"category_scores_gemma":[0.04313697,0.0007629265,0.0009535341,0.007446089,0.01811318,0.007614358,0.004371054,0.0116488,0.001416971],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09901982,"about_ca_system_score_gemma":0.2473664,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9614127,"about_ca_topic_score_gemma":0.9517156,"domain_scores_codex":[0.949107,0.006743243,0.002010864,0.002850506,0.03336317,0.005925153],"domain_scores_gemma":[0.9643588,0.01016364,0.001538824,0.002362483,0.01899228,0.002583907],"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":[0.00001354031,0.00001731719,0.0006631382,0.00009227344,0.000009971227,0.00005897475,0.001773741,0.0002592467,0.0001480881,0.8672383,0.1120981,0.01762724],"study_design_scores_gemma":[0.000009055722,0.00001168434,0.004285097,0.0005117535,0.00001821577,0.00005180516,0.001851553,0.0002858424,0.0002038503,0.02559947,0.9670989,0.0000728375],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.008042174,0.03309327,0.004664158,0.3313929,0.005763024,0.0002270444,0.001339115,0.000201232,0.6152771],"genre_scores_gemma":[0.3512183,0.03608084,0.02552755,0.2781061,0.002500735,0.0005433488,0.001573161,0.0003939047,0.3040561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9009802,"threshold_uncertainty_score":0.7184424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09115713470604404,"score_gpt":0.376707436595759,"score_spread":0.285550301889715,"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."}}