{"id":"W4365600402","doi":"10.48550/arxiv.2304.04914","title":"Regulatory Markets: The Future of AI Governance","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Regulation and Compliance Studies","field":"Business, Management and Accounting","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Legislature; Corporate governance; Business; Market regulation; Government regulation; Regulator; Control (management); Command and control; Financial regulation; Industrial organization; Public economics; Economics; Political science; Market economy; Finance; Engineering; Law; Management","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.03853623,0.0004417776,0.0007829223,0.002109917,0.004452393,0.01815433,0.002348984,0.009279593,0.008418533],"category_scores_gemma":[0.0370903,0.0004364145,0.0007081621,0.002780617,0.02926084,0.02427495,0.0045506,0.008637001,0.001112137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009027217,"about_ca_system_score_gemma":0.01818389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006714716,"about_ca_topic_score_gemma":0.003163041,"domain_scores_codex":[0.9799256,0.01128311,0.0006053371,0.002318235,0.004441489,0.001426253],"domain_scores_gemma":[0.9549255,0.03001259,0.003544503,0.003941468,0.005689545,0.001886359],"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":[0.000006118208,0.00001407199,0.0001812453,0.00003065073,0.000003349089,0.0000124576,0.0003994444,0.000619385,0.00004633011,0.9854046,0.003564441,0.009717902],"study_design_scores_gemma":[0.00001012401,0.00001226445,0.0002575543,0.0001150272,0.000002870604,0.00001427182,0.0005548942,0.001450275,0.00007348532,0.9393756,0.0581216,0.0000120758],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02520322,0.02082535,0.1244726,0.4851891,0.001697068,0.0001561974,0.0001744421,0.0004806341,0.3418014],"genre_scores_gemma":[0.8909554,0.01573022,0.0277423,0.03716109,0.003456057,0.000562139,0.0001591842,0.0002026188,0.0240309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03853623,"threshold_uncertainty_score":0.2038015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05381907922264166,"score_gpt":0.1786792944211428,"score_spread":0.1248602151985012,"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."}}