{"id":"W4403421377","doi":"10.1109/iiai-aai63651.2024.00052","title":"Minimizing Model Misclassification Using Regularized Loss Interpretability","year":2024,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Interpretability; Computer science; Artificial intelligence","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":[0.0167754,0.002702705,0.002278982,0.001759395,0.00101604,0.003669685,0.003420951,0.002884801,0.002423256],"category_scores_gemma":[0.05994634,0.0008267018,0.001467506,0.0009126064,0.002430024,0.004389551,0.004661586,0.005344939,0.0007407113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00218344,"about_ca_system_score_gemma":0.002427031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001928948,"about_ca_topic_score_gemma":0.001947039,"domain_scores_codex":[0.9908313,0.004245551,0.0005303028,0.00167641,0.002170262,0.0005461382],"domain_scores_gemma":[0.9711688,0.01531309,0.002955844,0.007318422,0.002622327,0.0006214562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007310315,0.0004651243,0.008272024,0.0002458679,0.000216276,0.0004552356,0.0005722205,0.7319682,0.007250334,0.03049256,0.005372752,0.2139584],"study_design_scores_gemma":[0.00003232068,0.0001444786,0.0006406985,0.00003752874,0.00002788205,0.000104504,0.00003785069,0.9628016,0.003123878,0.03215281,0.0008760368,0.00002041346],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03449653,0.0003697636,0.9610738,0.0009254934,0.0001068154,0.00011679,0.00008061845,0.001182478,0.001647669],"genre_scores_gemma":[0.6735862,0.000261451,0.319298,0.0008484159,0.0002280798,0.0003111495,0.0005824786,0.0009438818,0.003940387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0167754,"threshold_uncertainty_score":0.08871788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03906841556291794,"score_gpt":0.3097947442682105,"score_spread":0.2707263287052926,"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."}}