{"id":"W4296708919","doi":"10.1145/3563691","title":"Explainable Convolutional Neural Networks: A Taxonomy, Review, and Future Directions","year":2022,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Taxonomy (biology); Generalization; Relevance (law); Perspective (graphical); Machine learning; Feature (linguistics); Interpretation (philosophy)","routes":{"ca_aff":true,"ca_fund":true,"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","sts"],"consensus_categories":[],"category_scores_codex":[0.005262788,0.0006813743,0.001818935,0.0003043734,0.001315497,0.0003768279,0.003065442,0.0002236496,0.0001963693],"category_scores_gemma":[0.0007178815,0.0006655076,0.000448318,0.002242206,0.0001302537,0.0004957028,0.00355627,0.001225842,0.00005028443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003341652,"about_ca_system_score_gemma":0.0003515761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002682907,"about_ca_topic_score_gemma":0.0000398612,"domain_scores_codex":[0.9925243,0.003684863,0.001108025,0.001345915,0.0004349492,0.0009019868],"domain_scores_gemma":[0.9942453,0.002711521,0.0007283153,0.001911007,0.0001708853,0.0002329856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[2.776027e-7,0.00004081259,0.00001229817,0.003335632,0.00007688328,0.00003735503,0.00003953683,0.0001371868,3.660409e-9,0.002575463,0.004532075,0.9892125],"study_design_scores_gemma":[0.00003740965,0.0000550816,0.00001953023,0.001602519,0.0001716323,0.0002962303,0.00002065964,0.01861938,3.847602e-8,0.0001430302,0.9784024,0.0006320429],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[5.093e-7,0.9166324,0.07848931,0.0004114894,0.00233618,0.001275642,0.0000178253,0.0004075213,0.0004291912],"genre_scores_gemma":[0.000006198513,0.991998,0.00581019,0.000422107,0.001007449,0.0003407029,0.000131777,0.00005617881,0.0002273704],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9885804,"threshold_uncertainty_score":0.9999847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09707560278384564,"score_gpt":0.3225294605025278,"score_spread":0.2254538577186821,"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."}}