{"id":"W4381885735","doi":"10.1109/ethics57328.2023.10155071","title":"Bridging Industry, Government, and Academia for Socially Responsible AI: The CSEAI Initiative","year":2023,"lang":"en","type":"article","venue":"","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Science Foundation","keywords":"General partnership; Outreach; Workforce; Government (linguistics); Public relations; Engineering ethics; Diversity (politics); Ethical standards; Workforce development; Business; Political science; Engineering","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.003377772,0.00007761223,0.0001084152,0.00002237082,0.001892014,0.0002800658,0.0002019157,0.0006076501,0.00005258313],"category_scores_gemma":[0.003918805,0.00006011089,0.00005042523,0.0003554877,0.0004433892,0.000343536,0.00009740357,0.0009004142,0.0000140372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001105677,"about_ca_system_score_gemma":0.001007228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00099078,"about_ca_topic_score_gemma":0.004730853,"domain_scores_codex":[0.9986012,0.0002213615,0.0001281308,0.0001542611,0.0005101215,0.0003849275],"domain_scores_gemma":[0.9982145,0.001401509,0.00006685936,0.00007089793,0.0001297994,0.0001163672],"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":[0.00002408739,0.000007849307,0.002603238,0.000006043201,0.00003401601,0.000001454081,0.04438206,0.000002549662,0.0001316444,0.8187517,0.1264344,0.007621005],"study_design_scores_gemma":[0.0006261902,0.0001047508,0.03795485,0.00004167321,0.00004168135,2.898861e-7,0.1071387,0.000298318,0.0005241755,0.5974351,0.2554818,0.0003524406],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1135814,0.00003810381,0.0002156315,0.7080424,0.000266797,0.0005815195,0.0000680423,0.0001646761,0.1770414],"genre_scores_gemma":[0.9590238,0.0003048795,0.0001197235,0.01658349,0.0007112114,0.0000319366,0.000002137741,0.00001452749,0.02320825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8454424,"threshold_uncertainty_score":0.9994074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.138258211963363,"score_gpt":0.4485184467239713,"score_spread":0.3102602347606083,"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."}}