{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001098443,0.0004637828,0.0004789817,0.0008575979,0.0003956643,0.001075839,0.002382013,0.0002284894,0.0003590503],"category_scores_gemma":[0.0004124572,0.0004304661,0.00008503295,0.005380524,0.000202513,0.005860145,0.001139395,0.0005753431,0.000339182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004609787,"about_ca_system_score_gemma":0.001199449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003909202,"about_ca_topic_score_gemma":0.002109445,"domain_scores_codex":[0.9955642,0.000262585,0.0009869626,0.001337258,0.0006339174,0.001215036],"domain_scores_gemma":[0.9967883,0.000301397,0.0001856778,0.001900625,0.0006464435,0.0001775826],"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":[0.0002853317,0.001047204,0.02929386,0.0003355893,0.00005966109,0.0002087939,0.007114747,0.02164272,0.001241294,0.8731892,0.006981483,0.05860015],"study_design_scores_gemma":[0.0008152692,0.0005381841,0.008192956,0.0008221636,0.00002597499,0.000007223714,0.00150301,0.8071036,0.08133052,0.05927073,0.03955736,0.000832949],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06261244,0.0003899959,0.8427759,0.02357721,0.0006089417,0.001367864,0.000001131745,0.0001959553,0.06847049],"genre_scores_gemma":[0.9346463,0.0001030616,0.01442749,0.00350649,0.00005963445,0.0001743598,0.00000293235,0.00002312232,0.04705662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8720338,"threshold_uncertainty_score":0.9999611,"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."}}