{"id":"W4388623306","doi":"10.1109/ro-man57019.2023.10309659","title":"Trust Calibration Through Intentional Errors: Designing Robot Errors to Decrease Children’s Trust Towards Robots","year":2023,"lang":"en","type":"article","venue":"","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Calibration; Computer science; Human–computer interaction; Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006588832,0.001366597,0.0004463349,0.0006885621,0.0008738963,0.002178777,0.002046415,0.00150221,0.003128738],"category_scores_gemma":[0.05020108,0.0006446787,0.000622345,0.0003053746,0.002174356,0.004113612,0.002722009,0.001751491,0.0009816249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228711,"about_ca_system_score_gemma":0.00197088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001681132,"about_ca_topic_score_gemma":0.003100385,"domain_scores_codex":[0.9935167,0.003477765,0.0004632403,0.0007358953,0.001308981,0.0004975102],"domain_scores_gemma":[0.9653969,0.0177515,0.006213367,0.004756195,0.004550376,0.001331614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001700278,0.003123749,0.1622313,0.003867378,0.0004579248,0.001924779,0.07673696,0.05610142,0.06206252,0.04406958,0.01191428,0.5758098],"study_design_scores_gemma":[0.0008478744,0.01399644,0.1474244,0.004776754,0.001747815,0.004176445,0.0749396,0.2880099,0.1578649,0.09704591,0.208139,0.001030929],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5658144,0.0006813398,0.4097869,0.003656657,0.0002408642,0.0007669925,0.0001530409,0.001857042,0.0170428],"genre_scores_gemma":[0.8164402,0.0003216954,0.1776518,0.000497288,0.0000268392,0.0006980612,0.0001339456,0.0002049311,0.004025129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006588832,"threshold_uncertainty_score":0.03484547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07892297027217163,"score_gpt":0.3769679620317929,"score_spread":0.2980449917596213,"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."}}