{"id":"W4388196638","doi":"10.20944/preprints202310.1673.v1","title":"AI and Regulation an Analysis","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Legal and Policy Issues","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Section (typography); Misrepresentation; Cognition; Set (abstract data type); Human intelligence; Cognitive science; Natural (archaeology); Illusion; Psychology; Computer science; Artificial intelligence; Sociology; Cognitive psychology; Political science; Law; History","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001303664,0.0001741196,0.0003346943,0.0002934178,0.0003292381,0.0001050761,0.0004142277,0.0003481702,0.000943383],"category_scores_gemma":[0.0002830049,0.0001809241,0.0001593599,0.0004944724,0.0002191188,0.0002063256,0.0007228462,0.0003932648,0.0007943402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008338454,"about_ca_system_score_gemma":0.0001748854,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06439917,"about_ca_topic_score_gemma":0.01552366,"domain_scores_codex":[0.9979588,0.0003824842,0.0002711389,0.0006895298,0.0004035546,0.0002944802],"domain_scores_gemma":[0.9987551,0.00006981082,0.00015876,0.0006798217,0.000125459,0.0002110222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008610356,0.00003011227,0.9544038,0.00003231989,0.0002714197,0.000002959689,0.03089767,0.0006617774,0.0001044041,0.01286817,0.0001200657,0.0005987403],"study_design_scores_gemma":[0.00005811334,0.000004693372,0.936698,0.00002311368,0.0003199694,1.164364e-7,0.0005450665,0.0005736549,0.0003612832,0.02639362,0.03477373,0.0002486652],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709731,0.00004504589,0.00007535942,0.007605385,0.000376098,0.0002886799,0.00002380753,0.0003974583,0.020215],"genre_scores_gemma":[0.9770203,0.00021846,0.00004981331,0.0002429202,0.000655835,0.00004076237,0.00007055696,0.00001811358,0.02168326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04887551,"threshold_uncertainty_score":0.9999837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2571769257992574,"score_gpt":0.4681825355088599,"score_spread":0.2110056097096024,"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."}}