{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006143255,0.0003184504,0.0003437867,0.002382376,0.01691157,0.009483698,0.004043341,0.003148168,0.008860444],"category_scores_gemma":[0.0249955,0.0003883961,0.0005411565,0.002650008,0.02101524,0.003451607,0.002439249,0.006180353,0.0003305937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09936327,"about_ca_system_score_gemma":0.1770436,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.976646,"about_ca_topic_score_gemma":0.9868798,"domain_scores_codex":[0.9942484,0.0016361,0.0001060908,0.0005161332,0.001857351,0.001635914],"domain_scores_gemma":[0.9851568,0.004525227,0.0005801938,0.001184011,0.006208152,0.002345688],"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":[0.00003824527,0.00008631784,0.007597727,0.00003687871,0.00001259005,0.0000759466,0.01577268,0.003103755,0.0003064921,0.9152125,0.01394088,0.04381606],"study_design_scores_gemma":[0.00009986916,0.0001050557,0.08410092,0.0004372366,0.0001170675,0.0002307749,0.04771365,0.03700836,0.002266935,0.5271969,0.3003915,0.0003317423],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2211203,0.001208463,0.01962166,0.08658671,0.0003785214,0.0001894641,0.000249102,0.0002317035,0.6704141],"genre_scores_gemma":[0.9752283,0.0003308919,0.002936333,0.002436492,0.00004467534,0.00003170864,0.00005790215,0.00002874736,0.0189051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09936327,"threshold_uncertainty_score":0.7209343,"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."}}