{"id":"W2806992506","doi":"10.3390/jrfm11020028","title":"Customer Preferences and Implicit Tradeoffs in Accident Scenarios for Self-Driving Vehicle Algorithms","year":2018,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Massachusetts Institute of Technology","keywords":"Self driving; Computer science; Respondent; Automotive industry; Process (computing); Software deployment; Selection (genetic algorithm); Scale (ratio); Accident (philosophy); Operations research; Marketing; Transport engineering; Computer security; Business; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004470075,0.0003565552,0.000303918,0.0004470373,0.0005619606,0.002816637,0.000545827,0.001284396,0.006957135],"category_scores_gemma":[0.02927092,0.0002030073,0.0004165644,0.0005718163,0.0005807148,0.001840637,0.0008105437,0.00114322,0.0004721468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153007,"about_ca_system_score_gemma":0.0003114123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00201333,"about_ca_topic_score_gemma":0.002446905,"domain_scores_codex":[0.9967207,0.002140086,0.0001520963,0.0001914485,0.0004803331,0.0003152431],"domain_scores_gemma":[0.9783357,0.01721312,0.00132182,0.0009017685,0.001360132,0.0008674862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009226405,0.002992564,0.5950637,0.0003609675,0.0004325604,0.001702551,0.007041795,0.2735725,0.006851054,0.03037531,0.003400881,0.0689797],"study_design_scores_gemma":[0.0002385338,0.003393457,0.2631984,0.00009765638,0.0001946174,0.0009698364,0.01496937,0.6854695,0.003736933,0.02323668,0.004260538,0.0002344246],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960484,0.00002572208,0.001380069,0.0001025691,0.00000288624,0.00001893667,0.00005208719,0.000005485276,0.002363937],"genre_scores_gemma":[0.999073,0.0000122758,0.0005491626,0.00001408176,0.000001771548,0.00001266683,0.00007369378,0.00000355119,0.0002597886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006957135,"threshold_uncertainty_score":0.02364033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008102373173911008,"score_gpt":0.2275512340562424,"score_spread":0.2194488608823314,"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."}}