{"id":"W4416237636","doi":"10.1109/aiot66900.2025.00046","title":"Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization","year":2025,"lang":"en","type":"article","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Guelph; York University","funders":"","keywords":"Entropy (arrow of time); Leverage (statistics); Regularization (linguistics); Suite; Trustworthiness; Differential privacy; Information privacy","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":[],"consensus_categories":[],"category_scores_codex":[0.0004296806,0.0001645408,0.0001595027,0.0002420443,0.0003422385,0.0003350725,0.001914055,0.0001000774,0.00009138184],"category_scores_gemma":[0.0002021853,0.0001663295,0.00005792546,0.001359778,0.00003875755,0.001269753,0.0009653416,0.0001236158,0.0002749373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001590631,"about_ca_system_score_gemma":0.0000968133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002765168,"about_ca_topic_score_gemma":0.00003663608,"domain_scores_codex":[0.9982235,0.00008035982,0.0003759888,0.0006085602,0.0002897964,0.0004218404],"domain_scores_gemma":[0.9980581,0.0001109832,0.0001027397,0.00139728,0.0002508345,0.00008000086],"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":[0.000005865945,0.00007124752,0.0004871662,0.00002981873,0.0000113917,0.000002972538,0.0003233391,0.001083279,0.03385271,0.9322405,0.003003536,0.02888821],"study_design_scores_gemma":[0.00007074767,0.00002196706,0.0002505951,0.00001828111,0.000004507065,0.000001739049,0.00008537928,0.5653951,0.202778,0.2122978,0.0189234,0.0001525143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002036478,0.00007717738,0.9642403,0.004994741,0.0002369891,0.0004642653,2.754197e-7,0.0004677669,0.02748204],"genre_scores_gemma":[0.8841227,0.00002188027,0.09821109,0.001289333,0.00008045644,0.0001738901,0.00001182997,0.00001535186,0.01607345],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8820862,"threshold_uncertainty_score":0.678272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109693602441782,"score_gpt":0.2611046088028431,"score_spread":0.2501352485586649,"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."}}