{"id":"W3165398545","doi":"10.1080/14494035.2021.1929728","title":"Steering the governance of artificial intelligence: national strategies in perspective","year":2021,"lang":"en","type":"article","venue":"Policy and Society","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Corporate governance; Perspective (graphical); Plural; Set (abstract data type); Multi-level governance; Preference; Dozen; Public administration; Political science; Sociology; Economics; Artificial intelligence; Management; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007608383,0.00004015479,0.00007504234,0.000006635561,0.00032762,0.0001072569,0.00008003672,0.00007878717,0.0000180057],"category_scores_gemma":[0.00118078,0.00003428598,0.00005952227,0.0002882321,0.0004968834,0.0001950147,0.00003138099,0.0001731868,5.75395e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001389492,"about_ca_system_score_gemma":0.001279087,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01502751,"about_ca_topic_score_gemma":0.008329158,"domain_scores_codex":[0.9993128,0.00007958544,0.000105593,0.00008480298,0.0002778671,0.0001393274],"domain_scores_gemma":[0.9992821,0.0003009195,0.00004578035,0.00003788981,0.0003048239,0.00002852875],"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":[8.882964e-7,0.000013957,0.00005814476,0.000003586915,0.000007697739,2.496917e-7,0.1717173,0.00003331123,0.00006925452,0.8273839,0.00005023156,0.0006614123],"study_design_scores_gemma":[0.00001952429,0.000005585919,0.004952433,0.00001085283,0.000001795121,1.665252e-7,0.3364134,0.0001086575,0.0001643616,0.6573474,0.0009334599,0.0000424246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4313898,0.001406417,0.0003971008,0.1419848,0.0002131628,0.0001918408,0.00004567494,0.00002680282,0.4243445],"genre_scores_gemma":[0.9970359,0.00165806,0.0001487379,0.0006114048,0.0003239526,0.000001910812,3.387463e-7,0.000002277464,0.0002174467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5656461,"threshold_uncertainty_score":0.9915315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0776350468668587,"score_gpt":0.4257093459994601,"score_spread":0.3480742991326015,"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."}}