{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T04:00:45Z","timestamp":1785038445565,"version":"3.55.0"},"publisher-location":"Cham","reference-count":66,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031440632","type":"print"},{"value":"9783031440649","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44064-9_21","type":"book-chapter","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T04:19:19Z","timestamp":1698553159000},"page":"397-420","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The Co-12 Recipe for\u00a0Evaluating Interpretable Part-Prototype Image Classifiers"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0558-3810","authenticated-orcid":false,"given":"Meike","family":"Nauta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6776-3868","authenticated-orcid":false,"given":"Christin","family":"Seifert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"21_CR1","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems (2018)"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Barredo Arrieta, A., et al.: Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58 (2020)","DOI":"10.1016\/j.inffus.2019.12.012"},{"issue":"2","key":"21_CR3","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1037\/0033-295X.94.2.115","volume":"94","author":"I Biederman","year":"1987","unstructured":"Biederman, I.: Recognition-by-components: a theory of human image understanding. Psychol. Rev. 94(2), 115 (1987)","journal-title":"Psychol. Rev."},{"key":"21_CR4","unstructured":"Borowski, J., et al.: Exemplary natural images explain CNN activations better than state-of-the-art feature visualization. In: Proceedings of the International Conference on Learning Representations (2021)"},{"key":"21_CR5","unstructured":"Brendel, W., Bethge, M.: Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet. In: Proceedings of the International Conference on Learning Representations (2019)"},{"key":"21_CR6","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2020.507973","volume":"3","author":"S Bruckert","year":"2020","unstructured":"Bruckert, S., Finzel, B., Schmid, U.: The next generation of medical decision support: a roadmap toward transparent expert companions. Front. Artif. Intell. 3, 507973 (2020)","journal-title":"Front. Artif. Intell."},{"key":"21_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/978-3-031-37660-3_38","volume-title":"Pattern Recognition, Computer Vision, and Image Processing","author":"G Carloni","year":"2023","unstructured":"Carloni, G., Berti, A., Iacconi, C., Pascali, M.A., Colantonio, S.: On the applicability of prototypical part learning in medical images: breast masses classification using ProtoPNet. In: Rousseau, J.J., Kapralos, B. (eds.) ICPR 2022. LNCS, vol. 13643, pp. 539\u2013557. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-37660-3_38"},{"key":"21_CR8","unstructured":"Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., Su, J.: This looks like that: deep learning for interpretable image recognition. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems, pp. 8928\u20138939 (2019)"},{"issue":"1","key":"21_CR9","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3390\/make5010006","volume":"5","author":"T Clement","year":"2023","unstructured":"Clement, T., Kemmerzell, N., Abdelaal, M., Amberg, M.: XAIR: a systematic metareview of explainable AI (XAI) aligned to the software development process. Mach. Learn. Knowl. Extr. 5(1), 78\u2013108 (2023)","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"21_CR10","unstructured":"Colin, J., Fel, T., Cadene, R., Serre, T.: What I cannot predict, I do not understand: a human-centered evaluation framework for explainability methods. In: Advances in Neural Information Processing Systems (2022)"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Das, S., Xu, P., Dai, Z., Endert, A., Ren, L.: Interpreting deep neural networks through prototype factorization. In: Fatta, G.D., Sheng, V.S., Cuzzocrea, A., Zaniolo, C., Wu, X. (eds.) Proceedings of the International Conference on Data Mining Workshops, ICDM Workshops, pp. 448\u2013457. IEEE (2020)","DOI":"10.1109\/ICDMW51313.2020.00068"},{"key":"21_CR12","series-title":"The Springer Series on Challenges in Machine Learning","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-98131-4_1","volume-title":"Explainable and Interpretable Models in Computer Vision and Machine Learning","author":"F Doshi-Velez","year":"2018","unstructured":"Doshi-Velez, F., Kim, B.: Considerations for evaluation and generalization in interpretable machine learning. In: Escalante, H.J., et al. (eds.) Explainable and Interpretable Models in Computer Vision and Machine Learning. TSSCML, pp. 3\u201317. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98131-4_1"},{"key":"21_CR13","unstructured":"Ehsan, U., et al.: The who in explainable AI: how AI background shapes perceptions of AI explanations. arXiv preprint arXiv:2107.13509 (2021)"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Fong, R., Vedaldi, A.: Net2Vec: quantifying and explaining how concepts are encoded by filters in deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8730\u20138738. IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00910"},{"key":"21_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109172","volume":"136","author":"S Gautam","year":"2023","unstructured":"Gautam, S., H\u00f6hne, M.M.-C., Hansen, S., Jenssen, R., Kampffmeyer, M.: This looks more like that: enhancing self-explaining models by prototypical relevance propagation. Pattern Recogn. 136, 109172 (2023)","journal-title":"Pattern Recogn."},{"key":"21_CR16","unstructured":"Ghorbani, A., Wexler, J., Zou, J.Y., Kim, B.: Towards automatic concept-based explanations. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems, pp. 9273\u20139282 (2019)"},{"issue":"3","key":"21_CR17","first-page":"50","volume":"38","author":"B Goodman","year":"2017","unstructured":"Goodman, B., Flaxman, S.: European union regulations on algorithmic decision-making and a \u201cright to explanation\u2019\u2019. AI Mag. 38(3), 50\u201357 (2017)","journal-title":"AI Mag."},{"key":"21_CR18","unstructured":"Goyal, Y., Feder, A., Shalit, U., Kim, B.: Explaining classifiers with causal concept effect (CaCE). arXiv preprint arXiv:1907.07165 (2019)"},{"key":"21_CR19","unstructured":"Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., Lee, S.: Counterfactual visual explanations. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the International Conference on Machine Learning, ICML 2019, Long Beach, California, USA, 9\u201315 June 2019, vol. 97, pp. 2376\u20132384. PMLR (2019)"},{"issue":"5","key":"21_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 1\u201342 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"21_CR21","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the International Conference on Machine Learning, vol. 70, pp. 1321\u20131330. PMLR (2017)"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Hase, P., Bansal, M.: Evaluating explainable AI: which algorithmic explanations help users predict model behavior? In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, 5\u201310 July 2020, pp. 5540\u20135552. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.491"},{"key":"21_CR23","unstructured":"Hase, P., Xie, H., Bansal, M.: The out-of-distribution problem in explainability and search methods for feature importance explanations. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 3650\u20133666. Curran Associates Inc. (2021)"},{"key":"21_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107383","volume":"110","author":"Y He","year":"2021","unstructured":"He, Y., Shen, Z., Cui, P.: Towards non-IID image classification: a dataset and baselines. Pattern Recogn. 110, 107383 (2021)","journal-title":"Pattern Recogn."},{"key":"21_CR25","unstructured":"Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Metrics for explainable AI: challenges and prospects. arXiv:1812.04608 [cs] (2019)"},{"key":"21_CR26","unstructured":"Hoffmann, A., Fanconi, C., Rade, R., Kohler, J.: This looks like that... does it? Shortcomings of latent space prototype interpretability in deep networks. arXiv preprint arXiv:2105.02968 (2021)"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., Baesens, B.: An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decis. Support Syst. 51(1) (2011)","DOI":"10.1016\/j.dss.2010.12.003"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Jacovi, A., Goldberg, Y.: Towards faithfully interpretable NLP systems: how should we define and evaluate faithfulness? In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, 5\u201310 July 2020, pp. 4198\u20134205. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.386"},{"key":"21_CR29","unstructured":"Jeyakumar, J.V., Noor, J., Cheng, Y., Garcia, L., Srivastava, M.B.: How can I explain this to you? An empirical study of deep neural network explanation methods. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems (2020)"},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., Wortman Vaughan, J.: Interpreting interpretability: understanding data scientists\u2019 use of interpretability tools for machine learning. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 1\u201314. Association for Computing Machinery, New York (2020)","DOI":"10.1145\/3313831.3376219"},{"key":"21_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-031-19775-8_17","volume-title":"Computer Vision","author":"SSY Kim","year":"2022","unstructured":"Kim, S.S.Y., Meister, N., Ramaswamy, V.V., Fong, R., Russakovsky, O.: HIVE: evaluating the human interpretability of visual explanations. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13672, pp. 280\u2013298. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19775-8_17"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Kulesza, T., Burnett, M., Wong, W.-K., Stumpf, S.: Principles of explanatory debugging to personalize interactive machine learning. In: Proceedings of the International Conference on Intelligent User Interfaces, pp. 126\u2013137. Association for Computing Machinery, New York (2015)","DOI":"10.1145\/2678025.2701399"},{"key":"21_CR33","unstructured":"Lakkaraju, H., Leskovec, J.: Confusions over time: an interpretable Bayesian model to characterize trends in decision making. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, pp. 3261\u20133269 (2016)"},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 951\u2013958 (2009)","DOI":"10.1109\/CVPR.2009.5206594"},{"key":"21_CR35","unstructured":"Liu, Y., Khandagale, S., White, C., Neiswanger, W.: Synthetic benchmarks for scientific research in explainable machine learning. In: NeurIPS Datasets and Benchmarks Track (2021)"},{"key":"21_CR36","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s13748-021-00269-9","volume":"11","author":"A Michel","year":"2021","unstructured":"Michel, A., Jha, S.K., Ewetz, R.: A survey on the vulnerability of deep neural networks against adversarial attacks. Progress Artif. Intell. 11, 131\u2013141 (2021). https:\/\/doi.org\/10.1007\/s13748-021-00269-9","journal-title":"Progress Artif. Intell."},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Mohammadjafari, S., Cevik, M., Thanabalasingam, M., Basar, A.: Using ProtoPNet for interpretable Alzheimer\u2019s disease classification. In: Canadian Conference on AI (2021)","DOI":"10.21428\/594757db.fb59ce6c"},{"key":"21_CR38","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-030-28954-6_13","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"G Montavon","year":"2019","unstructured":"Montavon, G.: Gradient-based vs. propagation-based explanations: an axiomatic comparison. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., M\u00fcller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 253\u2013265. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_13"},{"key":"21_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-030-69544-6_24","volume-title":"Computer Vision \u2013 ACCV 2020","author":"KK Nakka","year":"2021","unstructured":"Nakka, K.K., Salzmann, M.: Towards robust fine-grained recognition by maximal separation of discriminative features. In: Ishikawa, H., Liu, C.-L., Pajdla, T., Shi, J. (eds.) ACCV 2020. LNCS, vol. 12627, pp. 391\u2013408. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-69544-6_24"},{"key":"21_CR40","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/978-3-030-93736-2_34","volume-title":"Machine Learning and Principles and Practice of Knowledge Discovery in Databases","author":"M Nauta","year":"2021","unstructured":"Nauta, M., Jutte, A., Provoost, J., Seifert, C.: This looks like that, because ... explaining prototypes for interpretable image recognition. In: Kamp, M., et al. (eds.) ECML PKDD 2021. CCIS, vol. 1524, pp. 441\u2013456. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-93736-2_34"},{"key":"21_CR41","doi-asserted-by":"crossref","unstructured":"Nauta, M., Schl\u00f6tterer, J., van Keulen, M., Seifert, C.: PIP-Net: patch-based intuitive prototypes for interpretable image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2744\u20132753 (2023)","DOI":"10.1109\/CVPR52729.2023.00269"},{"key":"21_CR42","doi-asserted-by":"crossref","unstructured":"Nauta, M., et al.: From anecdotal evidence to quantitative evaluation methods: a systematic review on evaluating explainable AI. ACM Comput. Surv. (2023)","DOI":"10.1145\/3583558"},{"key":"21_CR43","doi-asserted-by":"crossref","unstructured":"Nauta, M., van Bree, R., Seifert, C.: Neural prototype trees for interpretable fine-grained image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14933\u201314943 (2021)","DOI":"10.1109\/CVPR46437.2021.01469"},{"key":"21_CR44","unstructured":"Nguyen, G., Kim, D., Nguyen, A.: The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 26422\u201326436. Curran Associates Inc. (2021)"},{"key":"21_CR45","doi-asserted-by":"crossref","unstructured":"Papenmeier, A., Kern, D., Englebienne, G., Seifert, C.: It\u2019s complicated: the relationship between user trust, model accuracy and explanations in AI. ACM Trans. Comput.-Hum. Interact. 29(4) (2022)","DOI":"10.1145\/3495013"},{"key":"21_CR46","unstructured":"Pearce, T., Brintrup, A., Zhu, J.: Understanding softmax confidence and uncertainty. arXiv preprint arXiv:2106.04972 (2021)"},{"key":"21_CR47","unstructured":"Rong, Y., Leemann, T., Borisov, V., Kasneci, G., Kasneci, E.: A consistent and efficient evaluation strategy for attribution methods. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (eds.) Proceedings of the International Conference on Machine Learning, vol. 162, pp. 18770\u201318795. PMLR (2022)"},{"issue":"5","key":"21_CR48","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206\u2013215 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"21_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/978-3-031-19775-8_21","volume-title":"Computer Vision","author":"D Rymarczyk","year":"2022","unstructured":"Rymarczyk, D., Struski, \u0141, G\u00f3rszczak, M., Lewandowska, K., Tabor, J., Zieli\u0144ski, B.: Interpretable image classification with differentiable prototypes assignment. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, pp. 351\u2013368. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19775-8_21"},{"key":"21_CR50","doi-asserted-by":"crossref","unstructured":"Rymarczyk, D., Struski, L., Tabor, J., Zieli\u0144ski, B.: ProtoPShare: prototypical parts sharing for similarity discovery in interpretable image classification. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1420\u20131430. Association for Computing Machinery, New York (2021)","DOI":"10.1145\/3447548.3467245"},{"key":"21_CR51","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: IEEE International Conference on Computer Vision, ICCV, pp. 618\u2013626. IEEE Computer Society (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"21_CR52","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.neunet.2022.03.034","volume":"151","author":"G Singh","year":"2022","unstructured":"Singh, G.: Think positive: an interpretable neural network for image recognition. Neural Netw. 151, 178\u2013189 (2022)","journal-title":"Neural Netw."},{"key":"21_CR53","doi-asserted-by":"publisher","first-page":"85198","DOI":"10.1109\/ACCESS.2021.3087583","volume":"9","author":"G Singh","year":"2021","unstructured":"Singh, G., Yow, K.-C.: An interpretable deep learning model for Covid-19 detection with chest X-ray images. IEEE Access 9, 85198\u201385208 (2021)","journal-title":"IEEE Access"},{"key":"21_CR54","doi-asserted-by":"crossref","unstructured":"Sinhamahapatra, P., Heidemann, L., Monnet, M., Roscher, K.: Towards human-interpretable prototypes for visual assessment of image classification models. arXiv preprint arXiv:2211.12173 (2022)","DOI":"10.5220\/0011894900003417"},{"key":"21_CR55","unstructured":"Summers, C., Dinneen, M.J.: Nondeterminism and instability in neural network optimization. In: Meila, M., Zhang, T. (eds.) Proceedings of the International Conference on Machine Learning, vol. 139, pp. 9913\u20139922. PMLR (2021)"},{"key":"21_CR56","doi-asserted-by":"crossref","unstructured":"Vilone, G., Longo, L.: Notions of explainability and evaluation approaches for explainable artificial intelligence. Inf. Fusion 76 (2021)","DOI":"10.1016\/j.inffus.2021.05.009"},{"key":"21_CR57","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-031-16437-8_2","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"C Wang","year":"2022","unstructured":"Wang, C., et al.: Knowledge distillation to ensemble global and interpretable prototype-based mammogram classification models. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13433, pp. 14\u201324. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_2"},{"key":"21_CR58","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, H., Wang, X., Jing, L.: Interpretable image recognition by constructing transparent embedding space. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 895\u2013904 (2021)","DOI":"10.1109\/ICCV48922.2021.00093"},{"issue":"4","key":"21_CR59","doi-asserted-by":"publisher","first-page":"339","DOI":"10.17705\/1thci.00101","volume":"9","author":"PA Williams","year":"2017","unstructured":"Williams, P.A., Jenkins, J., Valacich, J., Byrd, M.D.: Measuring actual behaviors in HCI research-a call to action and an example. AIS Trans. Hum.-Comput. Interact. 9(4), 339\u2013352 (2017)","journal-title":"AIS Trans. Hum.-Comput. Interact."},{"key":"21_CR60","doi-asserted-by":"crossref","unstructured":"Xie, W., Li, X.-H., Cao, C.C., Zhang, N.L.: ViT-CX: causal explanation of vision transformers. arXiv preprint arXiv:2211.03064 (2022)","DOI":"10.24963\/ijcai.2023\/174"},{"key":"21_CR61","doi-asserted-by":"crossref","unstructured":"Xu-Darme, R., Qu\u00e9not, G., Chihani, Z., Rousset, M.-C.: Sanity checks and improvements for patch visualisation in prototype-based image classification. working paper or preprint (2023)","DOI":"10.1109\/CVPRW59228.2023.00377"},{"key":"21_CR62","unstructured":"Xue, M., et al.: ProtoPFormer: concentrating on prototypical parts in vision transformers for interpretable image recognition. arXiv preprint arXiv:2208.10431 (2022)"},{"key":"21_CR63","unstructured":"Yang, M., Kim, B.: Benchmarking attribution methods with relative feature importance. CoRR, abs\/1907.09701 (2019)"},{"key":"21_CR64","unstructured":"Yeh, C., Kim, B., Arik, S.\u00d6., Li, C., Pfister, T., Ravikumar, P.: On completeness-aware concept-based explanations in deep neural networks. In: Advances in Neural Information Processing Systems (2020)"},{"key":"21_CR65","doi-asserted-by":"crossref","unstructured":"Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: a survey on methods and metrics. Electronics 10(5) (2021)","DOI":"10.3390\/electronics10050593"},{"key":"21_CR66","unstructured":"Zhuang, D., Zhang, X., Song, S., Hooker, S.: Randomness in neural network training: characterizing the impact of tooling. In: Marculescu, D., Chi, Y., Wu, C. (eds.) Proceedings of the Machine Learning and Systems, vol. 4, pp. 316\u2013336 (2022)"}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44064-9_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T04:22:55Z","timestamp":1698553375000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44064-9_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440632","9783031440649"],"references-count":66,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44064-9_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/xaiworldconference.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"220","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}