{"id":"W4285606202","doi":"10.24963/ijcai.2022/811","title":"Ethics and Governance of Artificial Intelligence: A Survey of Machine Learning Researchers (Extended Abstract)","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute on Governance","funders":"University of Oxford; Open Philanthropy Project; Georgetown University; Canadian Institute for Advanced Research","keywords":"Relevance (law); Corporate governance; Artificial intelligence; Work (physics); Political science; Public relations; Public sector; Private sector; Computer science; Engineering; Management; Law; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.03021672,0.0001285647,0.0004922347,0.002724511,0.003018573,0.005698989,0.0006117643,0.00168761,0.002375828],"category_scores_gemma":[0.09030905,0.0005135483,0.0003400928,0.003870327,0.003208145,0.00505069,0.002727382,0.002332358,0.0004342745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002695121,"about_ca_system_score_gemma":0.004594008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005215354,"about_ca_topic_score_gemma":0.006071655,"domain_scores_codex":[0.9760577,0.01501131,0.002525487,0.001100014,0.003647428,0.001658077],"domain_scores_gemma":[0.8164088,0.0958949,0.04928804,0.004876078,0.01849885,0.01503334],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001381578,0.0002104959,0.7929392,0.0003917545,0.00007743092,0.0004295462,0.152005,0.0001979647,0.0006250525,0.004673467,0.01323453,0.03507737],"study_design_scores_gemma":[0.00004222496,0.0003217449,0.4296435,0.001168833,0.00003789731,0.00111559,0.5060226,0.001115159,0.0004127183,0.002496824,0.05750717,0.0001156906],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760172,0.002455325,0.0004580033,0.01642492,0.0000715842,0.00006172893,0.0001688205,0.00001045929,0.004332072],"genre_scores_gemma":[0.9939252,0.001953291,0.0003887692,0.002834243,0.00006587322,0.00007711474,0.0001001449,0.00001222324,0.000643282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9983124,"threshold_uncertainty_score":0.1598032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2634526621512286,"score_gpt":0.41029010875163,"score_spread":0.1468374466004014,"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."}}