{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002365764,0.00007015257,0.00007635737,0.00004282716,0.0005872508,0.00004949453,0.0002650052,0.00007120021,0.0006848535],"category_scores_gemma":[0.0002583429,0.00004817119,0.00005807231,0.0001403185,0.0002042636,0.0003627067,0.00001357052,0.0003979903,0.0002867946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001125619,"about_ca_system_score_gemma":0.001017373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007303397,"about_ca_topic_score_gemma":0.009949736,"domain_scores_codex":[0.997633,0.0001679952,0.0001912469,0.0001195737,0.0003442126,0.001543957],"domain_scores_gemma":[0.9995295,0.0000954843,0.0001054886,0.00009179753,0.00008712201,0.00009060818],"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":[0.0000140655,0.00001741899,0.0009584503,1.901428e-7,0.00001365728,3.526591e-7,0.0004807645,0.000001054779,0.003971859,0.7913061,0.001328791,0.2019073],"study_design_scores_gemma":[0.00008242343,0.00007144161,0.00005272885,0.000009678987,0.000006994973,0.00001341241,0.001452709,0.000009003437,0.001400341,0.7855281,0.2112659,0.0001073081],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3276452,0.005930863,0.4819312,0.08193595,0.002130078,0.0005244977,0.00001358883,0.0003542922,0.09953439],"genre_scores_gemma":[0.9725568,0.002648357,0.00003639911,0.00008447678,0.0004863618,0.000001751354,5.901048e-7,0.00001046916,0.0241748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6449116,"threshold_uncertainty_score":0.7498665,"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."}}