{"id":"W4309865903","doi":"10.1007/s00146-022-01591-z","title":"Toward safe AI","year":2022,"lang":"it","type":"article","venue":"AI & Society","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Risk analysis (engineering); Normative; Artificial intelligence; Management science; Machine learning; Data 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004435843,0.0010113,0.0007863606,0.001115533,0.00174251,0.004690476,0.001806412,0.002901722,0.01615357],"category_scores_gemma":[0.01688795,0.0005686691,0.0008830648,0.000508533,0.007717909,0.007233086,0.006034958,0.008975397,0.005587982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001584635,"about_ca_system_score_gemma":0.001823572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001660608,"about_ca_topic_score_gemma":0.001158663,"domain_scores_codex":[0.9974397,0.0009203125,0.00007626339,0.0004398587,0.0009483834,0.0001754281],"domain_scores_gemma":[0.9926404,0.003304413,0.0002973952,0.002327363,0.001048177,0.0003821694],"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":[0.00001627136,0.0000187534,0.0001502144,0.00003764543,0.00001475603,0.0000206823,0.0001327767,0.007351586,0.0003409486,0.9658974,0.008630197,0.01738867],"study_design_scores_gemma":[0.000004341226,0.000009689455,0.00004130169,0.00002905211,0.000004626988,0.00001840704,0.00004043855,0.01773814,0.0004116811,0.9551803,0.02651528,0.000006771765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007365273,0.003355126,0.7589203,0.03476501,0.001204803,0.00008865891,0.0002601827,0.00137101,0.1926697],"genre_scores_gemma":[0.6015095,0.005391646,0.2347113,0.01307036,0.002093938,0.0003940969,0.0008685504,0.001202496,0.1407581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01615357,"threshold_uncertainty_score":0.05403906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02187714235703819,"score_gpt":0.2800508867500709,"score_spread":0.2581737443930328,"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."}}