{"id":"W4401450379","doi":"10.2139/ssrn.4892851","title":"Generative AI in American and Canadian Courts: A “Training” Approach to Regulation","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Generative grammar; Training (meteorology); Artificial intelligence; Political science; Computer science; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002209142,0.00008336392,0.0001130622,0.0002735924,0.0003888541,0.0002473034,0.0001430029,0.00004940238,0.00002076542],"category_scores_gemma":[0.0001391764,0.00008169378,0.00002843998,0.0005648462,0.000230447,0.0002473884,0.000009867538,0.0008371817,0.00002037079],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002329763,"about_ca_system_score_gemma":0.006367464,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2052869,"about_ca_topic_score_gemma":0.9081576,"domain_scores_codex":[0.9977347,0.0001648477,0.0001762376,0.0001984884,0.0002220971,0.001503653],"domain_scores_gemma":[0.9995663,0.00004894386,0.00003070182,0.00005603634,0.00005708071,0.0002409046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004245387,0.000008388694,0.000270903,8.964612e-7,0.00001675807,0.000003563085,0.03760169,0.0002009032,0.00005860072,0.8530441,0.000231816,0.1085581],"study_design_scores_gemma":[0.00005215149,0.0002056957,0.000905156,0.0000450163,0.00001638432,0.00007527911,0.1292667,0.004475886,0.0000365297,0.7557731,0.1088593,0.0002888381],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7724872,0.003057259,0.07819286,0.06461604,0.0009719721,0.0008165586,0.000007835331,0.0001303948,0.07971992],"genre_scores_gemma":[0.9969129,0.0003930892,0.0005334067,0.0005912784,0.0004781986,0.000008994034,0.000001186175,0.00001231539,0.001068606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7028707,"threshold_uncertainty_score":0.9992655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03078435088248827,"score_gpt":0.3354586936477029,"score_spread":0.3046743427652147,"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."}}