{"id":"W4390338706","doi":"10.1038/s41598-023-49854-z","title":"On the interpretability of part-prototype based classifiers: a human centric analysis","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Interpretability; Computer science; Perspective (graphical); Representation (politics); Machine learning; Artificial intelligence; Set (abstract data type); Reliability (semiconductor); Black box; Task (project management); Limit (mathematics); Data mining; Systems engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04463499,0.001714851,0.001666183,0.004412599,0.001384358,0.006652806,0.002319403,0.003283212,0.003331228],"category_scores_gemma":[0.2224721,0.0006822974,0.001496633,0.002367062,0.004735623,0.008675305,0.003768246,0.003448191,0.0004906895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002317405,"about_ca_system_score_gemma":0.001097913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003244512,"about_ca_topic_score_gemma":0.002610911,"domain_scores_codex":[0.9529198,0.0320564,0.001408198,0.005585148,0.007166356,0.0008641039],"domain_scores_gemma":[0.6952822,0.2560836,0.01367621,0.02256758,0.01084916,0.001541119],"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":[0.004407791,0.0009391694,0.1165349,0.002367087,0.001861614,0.001238928,0.01465324,0.1435355,0.0190996,0.08304148,0.01088323,0.6014375],"study_design_scores_gemma":[0.0001685422,0.001221521,0.04432858,0.0004313855,0.0004270842,0.001056915,0.003083979,0.7729727,0.01110039,0.1593258,0.005649119,0.0002339754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3088246,0.003304981,0.6696733,0.00291725,0.0002238513,0.0007329364,0.0007436613,0.001103221,0.01247628],"genre_scores_gemma":[0.920713,0.0003251599,0.07699987,0.0003046966,0.00008883115,0.0002173861,0.0004783896,0.0001692409,0.0007034505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04463499,"threshold_uncertainty_score":0.2360553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0491905525721428,"score_gpt":0.3001583060939814,"score_spread":0.2509677535218386,"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."}}