{"id":"W3035856201","doi":"10.1109/icpr48806.2021.9412303","title":"A generalizable saliency map-based interpretation of model outcome","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interpretability; Artificial intelligence; Computer science; Salient; Outcome (game theory); Class (philosophy); Machine learning; Pixel; Property (philosophy); Artificial neural network; Perspective (graphical); Sensitivity (control systems); Exploit; Pattern recognition (psychology); Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004839011,0.0002967336,0.0004921238,0.0002671103,0.00006013772,0.0003213825,0.001850847,0.0002438183,0.0001062637],"category_scores_gemma":[0.0001158088,0.000295225,0.000281961,0.0003423326,0.00006142729,0.0004438401,0.001514046,0.0003346994,0.00004549566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001360506,"about_ca_system_score_gemma":0.0006154009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00079262,"about_ca_topic_score_gemma":0.0001973527,"domain_scores_codex":[0.9972808,0.0001264538,0.0008901476,0.0008398758,0.0005032107,0.0003595087],"domain_scores_gemma":[0.9973072,0.00008455767,0.000362624,0.001628015,0.0005149181,0.0001026743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000533506,0.0001168124,0.0002859332,0.0002384961,0.00001867032,0.00001175658,0.001133641,0.9389966,0.001121834,0.05564784,0.000311399,0.002111707],"study_design_scores_gemma":[0.000045676,0.0000281949,0.00001196778,0.000109152,0.00001180666,8.345721e-7,0.00008632021,0.92964,0.04966888,0.02008867,0.00003008883,0.0002784237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01037292,0.0001779333,0.9829798,0.0007758265,0.0008351041,0.0002983492,0.000006240118,0.0002092847,0.0043445],"genre_scores_gemma":[0.6301934,0.000007776986,0.3680427,0.0005741912,0.00002034706,0.00004911772,0.000023526,0.00001569668,0.001073303],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6198204,"threshold_uncertainty_score":0.99995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06865303010564977,"score_gpt":0.3259663453591399,"score_spread":0.2573133152534901,"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."}}