{"id":"W2985327985","doi":"10.2478/nimmir-2019-0015","title":"The Thorny Challenge of Making Moral Machines: Ethical Dilemmas with Self-Driving Cars","year":2019,"lang":"en","type":"article","venue":"NIM Marketing Intelligence Review","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outrage; Harm; Enforcement; Control (management); Moral dilemma; Business; Law and economics; Computer science; Political science; Psychology; Sociology; Social psychology; Law; Artificial intelligence; Politics","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":[0.0250317,0.0004232633,0.0006655929,0.001556132,0.003554238,0.007351137,0.001521585,0.006950845,0.003020158],"category_scores_gemma":[0.02497645,0.0002607815,0.0004512919,0.0009643013,0.02671733,0.008373094,0.00324249,0.008685272,0.0005004017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004294463,"about_ca_system_score_gemma":0.006246214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003403869,"about_ca_topic_score_gemma":0.005584971,"domain_scores_codex":[0.9729216,0.02239637,0.0003761222,0.0005352439,0.003135345,0.0006352751],"domain_scores_gemma":[0.9595069,0.03280009,0.002032186,0.0008791709,0.003783525,0.0009980921],"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":[0.0000313969,0.00006380008,0.000492086,0.00084979,0.00004533069,0.0003424688,0.009860675,0.001046016,0.0001798693,0.8777978,0.02945003,0.07984065],"study_design_scores_gemma":[0.0000266222,0.00008322735,0.001263703,0.002963482,0.00002220864,0.0006914595,0.0194288,0.001489785,0.0003550978,0.6289837,0.3446424,0.000049481],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03569544,0.2025862,0.01943918,0.6097034,0.00385935,0.00008140654,0.00003289009,0.00002972783,0.1285724],"genre_scores_gemma":[0.7809016,0.1167499,0.009227658,0.07634782,0.004406621,0.0001855075,0.00002935908,0.00005905036,0.01209249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0250317,"threshold_uncertainty_score":0.1323819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04718483052040479,"score_gpt":0.3838646533929182,"score_spread":0.3366798228725134,"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."}}