{"id":"W4416962054","doi":"10.1109/pst65910.2025.11268815","title":"Privacy Preservation with Noise in Explainable AI","year":2025,"lang":"","type":"article","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transparency (behavior); Bottleneck; Trustworthiness; Process (computing); Inference; Information privacy; Cloud computing","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":[0.01341802,0.0008022732,0.001032611,0.00136178,0.001736949,0.004534692,0.002381595,0.002451526,0.001566932],"category_scores_gemma":[0.09104243,0.0006412541,0.001588862,0.001281768,0.006423168,0.008061568,0.006201392,0.004818994,0.000286324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002096971,"about_ca_system_score_gemma":0.002703884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001416815,"about_ca_topic_score_gemma":0.0009714188,"domain_scores_codex":[0.9774753,0.01245444,0.001042679,0.002874695,0.005027088,0.00112581],"domain_scores_gemma":[0.870359,0.0890356,0.00740841,0.0284151,0.003936803,0.0008451231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008790089,0.0002717045,0.01298605,0.0005447228,0.0003478141,0.0008223171,0.004172651,0.1655779,0.01332653,0.6682096,0.00251216,0.1303495],"study_design_scores_gemma":[0.0000729407,0.0001331726,0.001435253,0.0001074629,0.0001169606,0.0003757115,0.000325021,0.3222483,0.01215849,0.657653,0.005303231,0.00007049256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1187181,0.0007703039,0.8712692,0.003238653,0.0000645005,0.0001414913,0.0002375038,0.0009777885,0.004582381],"genre_scores_gemma":[0.9077113,0.0002623583,0.09016868,0.000492254,0.00006479711,0.0001306851,0.0001775677,0.00009062164,0.0009018535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01341802,"threshold_uncertainty_score":0.07096213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377186955407731,"score_gpt":0.2905466310788237,"score_spread":0.2667747615247464,"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."}}