{"id":"W4320024021","doi":"10.1109/bigdata55660.2022.10020313","title":"The Analysis and Development of an XAI Process on Feature Contribution Explanation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Consistency (knowledge bases); Computer science; Feature (linguistics); 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":[0.05738936,0.0015796,0.001224924,0.008872311,0.002479532,0.006342391,0.003744287,0.001925717,0.00373332],"category_scores_gemma":[0.1681436,0.001480523,0.002627977,0.005268311,0.003590911,0.01009434,0.006267645,0.004891819,0.001096754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004402451,"about_ca_system_score_gemma":0.008682775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005007095,"about_ca_topic_score_gemma":0.003878372,"domain_scores_codex":[0.9621729,0.02107815,0.002617621,0.003429597,0.009943916,0.0007579183],"domain_scores_gemma":[0.7786217,0.1443537,0.01000092,0.03077756,0.03496775,0.001278412],"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":[0.00021767,0.000527631,0.01933994,0.001457472,0.0002402644,0.0003930145,0.01592031,0.02868278,0.01213687,0.2535862,0.005108752,0.662389],"study_design_scores_gemma":[0.0001410145,0.0007250048,0.01361,0.001630101,0.0002804664,0.0009255852,0.005566709,0.5588713,0.05667326,0.2873986,0.07385308,0.0003247798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008737051,0.0001684107,0.9856923,0.0008166755,0.00002006271,0.0009372047,0.0001973323,0.001250887,0.002180149],"genre_scores_gemma":[0.04842373,0.000109656,0.9495083,0.00008739282,0.00001705462,0.0006830896,0.00036965,0.0002139627,0.0005870603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05738936,"threshold_uncertainty_score":0.3035076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2539979832914212,"score_gpt":0.3711565148971464,"score_spread":0.1171585316057251,"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."}}