{"id":"W4309206502","doi":"10.36227/techrxiv.21545478","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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Consistency (knowledge bases); Feature (linguistics); Computer science; Process (computing); Ranking (information retrieval); 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":[0.04037866,0.001340619,0.001076331,0.006725709,0.001937465,0.005842384,0.00338044,0.001840316,0.003868979],"category_scores_gemma":[0.1301637,0.00132896,0.00236874,0.003974961,0.003422867,0.008760994,0.005227177,0.004377238,0.001040542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004050704,"about_ca_system_score_gemma":0.006563208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004979592,"about_ca_topic_score_gemma":0.003914383,"domain_scores_codex":[0.9692506,0.01666046,0.002050953,0.00285333,0.008555065,0.0006295804],"domain_scores_gemma":[0.8437706,0.09544171,0.008012265,0.02677614,0.02498135,0.001017913],"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":[0.0001731991,0.0003861343,0.01577606,0.001015874,0.0001925122,0.0003508397,0.009342517,0.03482375,0.0119807,0.2640937,0.004468817,0.657396],"study_design_scores_gemma":[0.00009381928,0.0005052935,0.009637573,0.0009329665,0.000193151,0.0007349216,0.002786064,0.6591186,0.05300155,0.2194219,0.05335409,0.000220091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008437095,0.000154688,0.9866524,0.0007436403,0.00001812838,0.0005551647,0.0001494221,0.001214526,0.00207492],"genre_scores_gemma":[0.0568972,0.0001196671,0.9410882,0.0000861302,0.00001757637,0.0004600938,0.0003278581,0.0002354566,0.0007678633],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04037866,"threshold_uncertainty_score":0.2135453,"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."}}