{"id":"W4309203559","doi":"10.36227/techrxiv.21545478.v1","title":"The Analysis and Development of an XAI Process on Feature Contribution Explanation","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Consistency (knowledge bases); Feature (linguistics); Computer science; Ranking (information retrieval); Process (computing); Data mining; Feature selection; Artificial intelligence; Information retrieval","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":[0.05310299,0.001510411,0.001254409,0.008214303,0.002307956,0.006113702,0.003689472,0.002011734,0.003906456],"category_scores_gemma":[0.165166,0.001507802,0.002606726,0.004675625,0.003922112,0.009699679,0.006326328,0.005016488,0.001097536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004396565,"about_ca_system_score_gemma":0.007267452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004221974,"about_ca_topic_score_gemma":0.003190456,"domain_scores_codex":[0.9615085,0.02180292,0.002596585,0.003376458,0.009970863,0.0007445981],"domain_scores_gemma":[0.7956707,0.13152,0.009414418,0.03097608,0.03123734,0.001181584],"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":[0.0001875816,0.000443236,0.01606153,0.001281595,0.000231763,0.0003590619,0.01347348,0.031608,0.01233012,0.2973518,0.00419575,0.622476],"study_design_scores_gemma":[0.0001211451,0.0006216696,0.01113866,0.001258538,0.0002503709,0.0008636395,0.003907504,0.5508121,0.05645524,0.3122506,0.06204431,0.000276186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007536717,0.0001450117,0.9880702,0.0006417803,0.00001668373,0.0006665245,0.0001324226,0.0009658181,0.001824942],"genre_scores_gemma":[0.05002609,0.0001117557,0.9480666,0.00007690566,0.00001787299,0.0005710608,0.0002971878,0.0002254005,0.0006071732],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05310299,"threshold_uncertainty_score":0.2808388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.024819464223269,"score_gpt":0.3125622218725441,"score_spread":0.2877427576492751,"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."}}