{"id":"W6906623310","doi":"10.17632/9p5drwt9xz.1","title":"Canada's national artificial intelligence governance system: Dataset from interviews with 20 government leaders &amp; subject matter experts","year":2024,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Subject matter; Subject-matter expert; Subject (documents); Corporate governance; Government (linguistics); Thematic analysis; Public sector; Civil society","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002057048,0.001065319,0.0006989424,0.006459761,0.00331271,0.002707936,0.002236919,0.001218085,0.01754321],"category_scores_gemma":[0.01210553,0.0005994839,0.0005568481,0.01693414,0.0008413799,0.0008380229,0.001985331,0.001550387,0.009960527],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0326734,"about_ca_system_score_gemma":0.06350511,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9572321,"about_ca_topic_score_gemma":0.9804205,"domain_scores_codex":[0.996978,0.0004012521,0.0002208151,0.0003784561,0.001376633,0.0006448159],"domain_scores_gemma":[0.9874517,0.002129573,0.0006131831,0.001176095,0.007371989,0.001257345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005820232,0.00004327379,0.005030481,0.0004318562,0.0000221664,0.00006851169,0.0008157265,0.0003624543,0.0001258416,0.001511295,0.9852034,0.006326698],"study_design_scores_gemma":[0.000102694,0.00001177432,0.04802139,0.0004661219,0.00002820008,0.00005544651,0.002352564,0.0006355422,0.0003938391,0.0006621397,0.9472136,0.00005656841],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001837608,0.0000768235,0.0001397268,0.000171814,0.00001850096,0.00008293524,0.9949156,0.00009758079,0.002659528],"genre_scores_gemma":[0.003540725,0.00008992026,0.0007609857,0.00007641892,0.000006345004,0.0004730207,0.992683,0.0000348026,0.002334784],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9673266,"threshold_uncertainty_score":0.2370632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06415871562262168,"score_gpt":0.3306520817204636,"score_spread":0.2664933660978419,"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."}}