{"id":"W4398419418","doi":"10.7910/dvn/brec5m","title":"Appendix for \"U.S. Public Opinion on the Governance of Artificial Intelligence\" (AIES 2020)","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Legal and Policy Issues","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute on Governance","funders":"","keywords":"Public opinion; Corporate governance; Political science; Artificial intelligence; Computer science; Law; Management; Economics; Politics","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":[0.0009726842,0.0002314339,0.0003455227,0.00005753272,0.0003733529,0.0002060278,0.001515077,0.0002728727,0.01922807],"category_scores_gemma":[0.001613055,0.0001671528,0.0001783531,0.0002438919,0.0004619966,0.000318023,0.00021902,0.0003006576,0.06927073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000879944,"about_ca_system_score_gemma":0.0004228785,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007392657,"about_ca_topic_score_gemma":0.003740424,"domain_scores_codex":[0.9979777,0.0002109966,0.0003765864,0.0003684493,0.0006341074,0.0004321749],"domain_scores_gemma":[0.9977256,0.0007195062,0.0004373109,0.0009075059,0.0001175954,0.00009245452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004685641,0.00005602583,0.000001335822,0.00007288675,0.00003495127,9.391035e-7,0.0005336111,0.000002065888,0.000001316724,0.09667097,0.9012036,0.001375432],"study_design_scores_gemma":[0.00004023761,0.00008237418,0.00000269879,0.00009142524,0.00002181369,2.987084e-7,0.0009597503,0.000009841497,0.00004630148,0.001515404,0.9970296,0.0002002497],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001066047,0.000008550466,0.0000252194,0.00118299,0.002222232,0.0007394314,0.9942256,0.0000179449,0.001567363],"genre_scores_gemma":[0.0002705932,0.003168704,0.00004533279,0.0005822415,0.002018268,0.00005219907,0.9928293,0.00001457586,0.001018793],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.095826,"threshold_uncertainty_score":0.9992172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09351573410583396,"score_gpt":0.3503267129482316,"score_spread":0.2568109788423977,"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."}}