{"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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00166766,0.0001908983,0.0002084332,0.0003809528,0.0007296043,0.0004268448,0.006234177,0.00004925815,0.00006033423],"category_scores_gemma":[0.0003087125,0.0001635856,0.0000247359,0.0008142921,0.00008625969,0.001142208,0.001775117,0.0003554256,0.00001751127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001514487,"about_ca_system_score_gemma":0.0003966797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001019392,"about_ca_topic_score_gemma":0.001605774,"domain_scores_codex":[0.9967165,0.0002261623,0.0004401158,0.0009718127,0.001382412,0.000262992],"domain_scores_gemma":[0.9967681,0.0002245786,0.0003788739,0.002130371,0.0004056574,0.00009238906],"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":[0.0003562506,0.0005700326,0.0006175959,0.00001562569,0.0006535724,0.00003662926,0.001606149,0.001234936,0.0027786,0.08992799,0.004991137,0.8972115],"study_design_scores_gemma":[0.0006147005,0.0004017534,0.005026561,0.00005247209,0.0001004105,0.00002275932,0.003041417,0.866309,0.03197844,0.005367869,0.08639202,0.0006926274],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2616138,0.0001632976,0.6978266,0.01417523,0.007875231,0.001274059,0.01488414,0.0002463814,0.001941262],"genre_scores_gemma":[0.9857733,0.00006843157,0.001209893,0.0002779453,0.0001594196,0.00006967019,0.01229839,0.000008522232,0.0001344811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8965188,"threshold_uncertainty_score":0.9991426,"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."}}