{"id":"W4398133596","doi":"10.1126/science.adn0117","title":"Managing extreme AI risks amid rapid progress","year":2024,"lang":"en","type":"article","venue":"Science","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":266,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto; Schwartz/Reisman Emergency Medicine Institute; Vector Institute; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Risk analysis (engineering); Corporate governance; Computer science; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01931545,0.0008181896,0.0006293521,0.001338054,0.004455896,0.009775883,0.002372648,0.004920278,0.009506025],"category_scores_gemma":[0.05291529,0.000362108,0.0005518845,0.0009139617,0.003812018,0.00920295,0.009911368,0.006770146,0.003138193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002313569,"about_ca_system_score_gemma":0.01051619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000883758,"about_ca_topic_score_gemma":0.001799632,"domain_scores_codex":[0.9873334,0.005201789,0.0005957468,0.0008330506,0.004114927,0.001921037],"domain_scores_gemma":[0.9536501,0.02042091,0.007345527,0.004517647,0.007012433,0.007053243],"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":[0.0006470648,0.0009206341,0.02549025,0.001700057,0.0003698065,0.007650652,0.01610732,0.03951981,0.01408193,0.3988579,0.11094,0.3837147],"study_design_scores_gemma":[0.00004796714,0.0005903157,0.008971437,0.001056271,0.00005613925,0.001028503,0.02228742,0.01330652,0.003302193,0.7245973,0.2246143,0.0001416597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.1927394,0.005850332,0.3205868,0.1833744,0.004701657,0.001928249,0.0004124835,0.002380996,0.2880258],"genre_scores_gemma":[0.8588743,0.004336627,0.09584434,0.007753944,0.001340143,0.001123755,0.0003313524,0.0003398164,0.0300558],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.01931545,"threshold_uncertainty_score":0.1021511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1522338606013797,"score_gpt":0.45875427720543,"score_spread":0.3065204166040503,"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."}}