{"id":"W7117137071","doi":"10.2139/ssrn.5964045","title":"Explainable AI: XVARS Outperforms SHAP in Terms of Computational Speed and Sampling Robustness","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Robustness (evolution); Computation; Sampling (signal processing); Feature (linguistics); Sensitivity (control systems); Sample size determination; Explanatory power","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":[0.002655098,0.0007229511,0.001145445,0.0007828533,0.000575433,0.00234002,0.001432432,0.001586019,0.01863799],"category_scores_gemma":[0.01594963,0.0003036512,0.0007297923,0.0008566444,0.00121255,0.004878456,0.001760066,0.001732075,0.001467064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079635,"about_ca_system_score_gemma":0.001668701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001520235,"about_ca_topic_score_gemma":0.002118912,"domain_scores_codex":[0.9986903,0.0005681058,0.00005494045,0.0002796785,0.0002584712,0.0001485846],"domain_scores_gemma":[0.9893555,0.007405623,0.0004552758,0.002133576,0.0003146756,0.0003353895],"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":[0.003165669,0.000786454,0.005668788,0.0005297277,0.00035661,0.0001464028,0.0003669701,0.2762678,0.003928685,0.2353964,0.02859289,0.4447937],"study_design_scores_gemma":[0.0004267952,0.0003729513,0.001047441,0.00004102361,0.00006975869,0.00009450944,0.0001408211,0.643474,0.001888056,0.3487431,0.003680784,0.00002065527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6277744,0.002059573,0.2854858,0.005268281,0.0007045605,0.0003048024,0.001115037,0.00583273,0.07145482],"genre_scores_gemma":[0.9422225,0.0003391588,0.05079805,0.0003228452,0.0001626372,0.00006339986,0.0004842542,0.0003406283,0.005266488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01863799,"threshold_uncertainty_score":0.06235027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03008068128982423,"score_gpt":0.3037943334211514,"score_spread":0.2737136521313272,"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."}}