{"id":"W2560015915","doi":"10.2139/ssrn.2878950","title":"Regulation by Machine","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Vector Institute; Royal Ontario Museum; University of Toronto","funders":"","keywords":"Converse; Computer science; Artificial intelligence; Focus (optics); Ex-ante; Machine learning; Risk analysis (engineering); Business; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002565739,0.0003248041,0.0004437981,0.001074082,0.0009998088,0.003772252,0.001010131,0.002470593,0.01827243],"category_scores_gemma":[0.007592021,0.0002851858,0.0007794274,0.0006257625,0.005303243,0.003475551,0.001359513,0.002018496,0.002663213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00168964,"about_ca_system_score_gemma":0.001497484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001290682,"about_ca_topic_score_gemma":0.001102058,"domain_scores_codex":[0.997317,0.001039536,0.0000903571,0.0007579104,0.0005398842,0.0002553381],"domain_scores_gemma":[0.9937364,0.003122278,0.0005893369,0.001758133,0.0005467325,0.0002470852],"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":[0.00001002939,0.00002105354,0.0008156077,0.00002293637,0.00001838104,0.00003912097,0.0002074257,0.001549932,0.0003843447,0.982426,0.005374207,0.009130876],"study_design_scores_gemma":[0.00002081676,0.00002617074,0.001722097,0.00003780682,0.00001669515,0.00008563325,0.000173026,0.01481939,0.0008988287,0.9340732,0.04810753,0.00001874067],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05356046,0.001278048,0.1648637,0.02807916,0.0008342275,0.0001494753,0.0004212003,0.00064042,0.7501733],"genre_scores_gemma":[0.9285542,0.0004309186,0.01050945,0.003626597,0.0004291926,0.000179498,0.0001728535,0.00007377889,0.05602358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01827243,"threshold_uncertainty_score":0.06112736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388423754315206,"score_gpt":0.3061970066203449,"score_spread":0.2923127690771928,"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."}}