{"id":"W4401746721","doi":"10.1080/17579961.2024.2392930","title":"Generative AI in American and Canadian courts: a ‘training’ approach to regulation","year":2024,"lang":"en","type":"article","venue":"Law Innovation and Technology","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Saskatchewan","funders":"","keywords":"Generative grammar; Training (meteorology); Political science; Law; Computer science; Artificial intelligence; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000324706,0.00005395841,0.00008363327,0.0005420852,0.0001981279,0.00008358844,0.0000581038,0.0001051242,0.00001210249],"category_scores_gemma":[0.00011932,0.00005698305,0.000003394749,0.002534367,0.0007229852,0.0001199384,0.00001633561,0.0001320012,0.000006046768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001119384,"about_ca_system_score_gemma":0.000166925,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09237388,"about_ca_topic_score_gemma":0.4478953,"domain_scores_codex":[0.9993733,0.00002632186,0.0001477697,0.0001991009,0.00007148205,0.0001820687],"domain_scores_gemma":[0.9997556,0.00002237893,0.00002116606,0.000061768,0.00009865728,0.00004040395],"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":[7.490183e-7,0.000004050161,0.0003984354,0.000001718033,0.000002597962,0.000001422958,0.006009082,0.000003375028,0.0003685359,0.9317994,0.0002191035,0.06119157],"study_design_scores_gemma":[0.00003133738,0.00005289144,0.0006618071,0.00002335049,0.000002917458,0.00000418422,0.01438509,0.002468731,0.0006558963,0.4873253,0.4942384,0.0001500744],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7513473,0.00009917686,0.008200085,0.1193605,0.0002672231,0.000509796,0.000007543057,0.0003157723,0.1198926],"genre_scores_gemma":[0.9947477,0.000007705238,0.002844276,0.002080995,0.00005008276,0.00003235861,0.000005179655,0.000005487379,0.0002261997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4940193,"threshold_uncertainty_score":0.9136701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04715295533137928,"score_gpt":0.3481734023181994,"score_spread":0.3010204469868202,"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."}}