{"id":"W2823329128","doi":"10.24963/ijcai.2018/779","title":"Building Ethics into Artificial Intelligence","year":2018,"lang":"en","type":"article","venue":"","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Ng Teng Fong Charitable Foundation; Peking University; Nanyang Technological University; National Research Foundation Singapore; National Research Foundation","keywords":"Corporate governance; Taxonomy (biology); Field (mathematics); Engineering ethics; Computer science; Applications of artificial intelligence; Management science; Knowledge management; Artificial intelligence; Sociology; Engineering; Management","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":[0.02831979,0.001032229,0.0008171853,0.003506657,0.006217844,0.01265591,0.001621714,0.005251637,0.004562078],"category_scores_gemma":[0.03043982,0.0005732789,0.0007698668,0.00180276,0.06549735,0.01886554,0.008230145,0.008722691,0.001094396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007486156,"about_ca_system_score_gemma":0.009891518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0025871,"about_ca_topic_score_gemma":0.001834116,"domain_scores_codex":[0.9770483,0.01703818,0.0007343048,0.001502289,0.002754396,0.0009226774],"domain_scores_gemma":[0.9727844,0.01830298,0.001492128,0.003286577,0.002980036,0.001153789],"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":[0.000001457176,0.000004910224,0.00009424089,0.00002185105,0.000002271072,0.00001371878,0.001199975,0.0003233365,0.00002821237,0.9943046,0.0007954924,0.003209897],"study_design_scores_gemma":[0.000001890488,0.000003458051,0.00005710327,0.00006709193,0.000001533606,0.00001216575,0.0005136899,0.0004797365,0.00004275631,0.9748871,0.02392978,0.000003730674],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0204029,0.01023277,0.375893,0.1731919,0.00197978,0.000217068,0.0001053917,0.0002703215,0.4177069],"genre_scores_gemma":[0.845817,0.00746482,0.1135768,0.01279772,0.001938476,0.0005805026,0.0001148462,0.0002465798,0.01746332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02831979,"threshold_uncertainty_score":0.1497712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.219264058830526,"score_gpt":0.5043366901232331,"score_spread":0.2850726312927071,"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."}}