{"id":"W3208693767","doi":"10.1007/978-3-030-88900-5_27","title":"Explainable Artificial Intelligence (XAI): How the Visualization of AI Predictions Affects User Cognitive Load and Confidence","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in information systems and organisation","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"CLARITY; Computer science; Visualization; Transparency (behavior); Artificial intelligence; Cognitive load; Cognition; Presentation (obstetrics); Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007162843,0.000291346,0.0003523906,0.0002942108,0.0002884256,0.0008202059,0.0002450981,0.0003661856,0.00002559277],"category_scores_gemma":[0.0006990794,0.0002468913,0.00004306542,0.0002982188,0.0001783199,0.002144146,0.0001603416,0.0003487014,0.00001558183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001723925,"about_ca_system_score_gemma":0.0003114958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000214999,"about_ca_topic_score_gemma":0.0002804667,"domain_scores_codex":[0.9980977,0.0001096311,0.0006752965,0.0003243654,0.0005770333,0.0002160142],"domain_scores_gemma":[0.9974441,0.0006955604,0.0006245316,0.0003373697,0.0008374683,0.00006096259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001597553,0.00001209427,0.000105348,0.0004238315,0.00003387764,0.000003421339,0.01572083,0.002731214,0.0001619054,0.9582203,0.00004074274,0.02253044],"study_design_scores_gemma":[0.000550288,0.001027617,0.0006953996,0.007000386,0.0002538713,0.0004002249,0.01326062,0.6252368,0.1118017,0.2208027,0.01658553,0.002384899],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004905394,0.0006641018,0.9944364,0.0007789024,0.0005123907,0.0009619643,0.00002059222,0.00006544266,0.002069623],"genre_scores_gemma":[0.9983285,0.0003261531,0.0003310009,0.0002798899,0.0001165375,0.00004630222,0.00007065549,0.00001736167,0.0004835762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.997838,"threshold_uncertainty_score":0.9999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349838693404572,"score_gpt":0.2561526430772192,"score_spread":0.2326542561431735,"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."}}