{"id":"W3042234123","doi":"10.48550/arxiv.2007.09028","title":"Sequential Explanations with Mental Model-Based Policies","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interpretability; Computer science; Reinforcement learning; Proxy (statistics); Representation (politics); Selection (genetic algorithm); Mental representation; Baseline (sea); Machine learning; Artificial intelligence; Mental model; Cognitive psychology; Data science; Psychology; Cognitive science; Cognition","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":[0.009873793,0.0009390626,0.0004807383,0.0006159957,0.000537678,0.002251701,0.001388094,0.001640087,0.004416933],"category_scores_gemma":[0.08793554,0.0004784455,0.0006374515,0.0004318966,0.001455684,0.004116545,0.001995268,0.002119625,0.0004445643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001425894,"about_ca_system_score_gemma":0.001628474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001556833,"about_ca_topic_score_gemma":0.001833607,"domain_scores_codex":[0.9904898,0.006752643,0.0004155262,0.001198498,0.0008425443,0.0003008786],"domain_scores_gemma":[0.9053512,0.07580242,0.005498259,0.009815607,0.002236772,0.001295664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004739826,0.00233546,0.038845,0.001314851,0.0004724087,0.0005873629,0.01232159,0.3568434,0.03289096,0.1867443,0.005253636,0.3576512],"study_design_scores_gemma":[0.000485048,0.0005382137,0.004493713,0.0001427289,0.0001498503,0.0001245696,0.0006173164,0.8180506,0.01283706,0.1570197,0.005444098,0.00009714329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3096686,0.0002479762,0.6797546,0.001873583,0.00006185199,0.0006362372,0.0003154355,0.00218243,0.005259157],"genre_scores_gemma":[0.874079,0.00006816266,0.124281,0.0001544979,0.00001890404,0.0003804379,0.0001903243,0.0000730231,0.0007547454],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009873793,"threshold_uncertainty_score":0.05221826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1489738218570813,"score_gpt":0.2217217124871226,"score_spread":0.07274789063004128,"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."}}