{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T15:06:22Z","timestamp":1750691182862,"version":"3.41.0"},"publisher-location":"Cham","reference-count":60,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030779696"},{"type":"electronic","value":"9783030779702"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-77970-2_1","type":"book-chapter","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T10:05:06Z","timestamp":1623319506000},"page":"3-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The Methods and Approaches of\u00a0Explainable Artificial Intelligence"],"prefix":"10.1007","author":[{"given":"Mateusz","family":"Szczepa\u0144ski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Micha\u0142","family":"Chora\u015b","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marek","family":"Pawlicki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandra","family":"Pawlicka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","year":"2019","unstructured":"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. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6"},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"832","DOI":"10.3390\/electronics8080832","volume":"8","author":"T Miller","year":"2019","unstructured":"Miller, T.: Machine learning interpretability: a survey on methods and metrics. Electronics 8, 832 (2019)","journal-title":"Electronics"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"DV Carvalho","year":"2019","unstructured":"Carvalho, D.V., Pereira, E.M., Cardoso, J.S.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019)","journal-title":"Artif. Intell."},{"key":"1_CR4","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.future.2020.04.013","volume":"110","author":"M Pawlicki","year":"2020","unstructured":"Pawlicki, M., Chora\u015b, M., Kozik, R.: Defending network intrusion detection systems against adversarial evasion attacks. FGCS 110, 148\u2013154 (2020)","journal-title":"FGCS"},{"key":"1_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/978-3-030-50423-6_46","volume-title":"Computational Science \u2013 ICCS 2020","author":"M Chora\u015b","year":"2020","unstructured":"Chora\u015b, M., Pawlicki, M., Puchalski, D., Kozik, R.: Machine learning \u2013 the results are not the only thing that matters! What about security, explainability and fairness? In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12140, pp. 615\u2013628. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50423-6_46"},{"key":"1_CR6","doi-asserted-by":"publisher","first-page":"73127","DOI":"10.1109\/ACCESS.2020.2988359","volume":"8","author":"M Wang","year":"2020","unstructured":"Wang, M., Zheng, K., Yang, Y., Wang, X.: An explainable machine learning framework for intrusion detection systems. IEEE Access 8, 73127\u201373141 (2020)","journal-title":"IEEE Access"},{"key":"1_CR7","unstructured":"Vilone, G., Longo, L.: Explainable Artificial Intelligence: a Systematic Review (2020)"},{"key":"1_CR8","unstructured":"Xie, N., Ras, G., van Gerven, M., Doran, D.: Explainable Deep Learning: A Field Guide for the Uninitiated (2020)"},{"issue":"4","key":"1_CR9","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/s42256-020-0171-8","volume":"2","author":"J Stoyanovich","year":"2020","unstructured":"Stoyanovich, J., Van Bavel, J.J., West, T.V.: The imperative of interpretable machines. Nat. Mach. Intell. 2(4), 197\u2013199 (2020)","journal-title":"Nat. Mach. Intell."},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Roscher, R., Bohn, B., Duarte, M.F., Garcke, J.: Explainable Machine Learning for Scientific Insights and Discoveries, CoRR (2019)","DOI":"10.1109\/ACCESS.2020.2976199"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Tjoa, E., Guan, E.: A survey on explainable artificial intelligence: toward medical XAI. IEEE Trans. Neural Netw. Learn. Syst. (2020)","DOI":"10.1109\/TNNLS.2020.3027314"},{"issue":"5","key":"1_CR12","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.2214\/AJR.19.22145","volume":"214","author":"A Ghosh","year":"2020","unstructured":"Ghosh, A., Kandasamy, D.: Interpretable artificial intelligence: why and when. Am. J. Roentgenol. 214(5), 1137\u20131138 (2020)","journal-title":"Am. J. Roentgenol."},{"issue":"3","key":"1_CR13","doi-asserted-by":"publisher","first-page":"e190043","DOI":"10.1148\/ryai.2020190043","volume":"2","author":"M Reyes","year":"2020","unstructured":"Reyes, M., et al.: On the interpretability of artificial intelligence in radiology. Radiol. Artif. Intell. 2(3), e190043 (2020)","journal-title":"Radiol. Artif. Intell."},{"key":"1_CR14","unstructured":"https:\/\/cloud.google.com\/explainable-ai"},{"key":"1_CR15","unstructured":"Arya, V., et al.: One explanation does not fit all: a toolkit and taxonomy of AI explainability techniques (2019)"},{"issue":"6","key":"1_CR16","doi-asserted-by":"publisher","first-page":"27122","DOI":"10.4161\/cib.27122","volume":"6","author":"L Samhita","year":"2013","unstructured":"Samhita, L., Gross, H.: The \u201cClever Hans phenomenon\u2019\u2019 revisited. Commun. Integr. Biol. 6(6), 27122 (2013)","journal-title":"Commun. Integr. Biol."},{"key":"1_CR17","unstructured":"Greene, T.: AI now: predictive policing systems are flawed because they replicate and amplify racism. TNW (2020)"},{"issue":"2","key":"1_CR18","first-page":"40","volume":"38","author":"PM Asaro","year":"2019","unstructured":"Asaro, P.M.: AI ethics in predictive policing: from models of threat to an ethics of care. IEEE TSM 38(2), 40\u201353 (2019)","journal-title":"IEEE TSM"},{"key":"1_CR19","unstructured":"Wexler, R.: When a computer program keeps you in jail: how computers are harming criminal justice. New York Times (2017)"},{"key":"1_CR20","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"key":"1_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/978-3-030-50423-6_46","volume-title":"Computational Science \u2013 ICCS 2020","author":"M Chora\u015b","year":"2020","unstructured":"Chora\u015b, M., Pawlicki, M., Puchalski, D., Kozik, R.: Machine learning \u2013 the results are not the only thing that matters! what about security, explainability and fairness? In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12140, pp. 615\u2013628. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50423-6_46"},{"key":"1_CR22","unstructured":"Gandhi, M.: What exactly is meant by explainability and interpretability of AI? Analytics Vidhya (2020)"},{"key":"1_CR23","unstructured":"Taylor, M.E.: Intelligibility is a key component to trust in machine learning. Borealis AI (2019)"},{"key":"1_CR24","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":"1_CR25","unstructured":"Doshi-Velez, F., Been, K.: Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)"},{"key":"1_CR26","unstructured":"Honegger, M.: Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions. arXiv preprint arXiv:1808.05054 (2018)"},{"key":"1_CR27","unstructured":"Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach (2010)"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Liu, S., Zheng, H., Feng, Y., Li, W.: Prostate cancer diagnosis using deep learning with 3D multiparametric MRI. In: Medical Imaging2017: Computer-Aided Diagnosis (2017)","DOI":"10.1117\/12.2277121"},{"key":"1_CR29","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning (2016)"},{"key":"1_CR30","unstructured":"Lipton, Z.C.: The mythos of model interpretability. In: International Conference \u201cIn Machine Learning: Workshop on Human Interpretability in Machine Learning\u201d (2016)"},{"key":"1_CR31","doi-asserted-by":"crossref","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). In: ICCS, vol. 6 (2018)","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"1_CR32","unstructured":"Weina, J., Carpendale, S., Hamarneh, G., Gromala, D.: Bridging AI developers and end users: an end-user-centred explainable AI taxonomy and visual vocabularies. In: IEEE Vis (2019)"},{"key":"1_CR33","unstructured":"Chromik, M., Schuessler, M.: A taxonomy for human subject evaluation of black-box explanations in XAI. In: ExSS-ATEC@ IUI (2020)"},{"key":"1_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-030-29726-8_2","volume-title":"Machine Learning and Knowledge Extraction","author":"A Blanco-Justicia","year":"2019","unstructured":"Blanco-Justicia, A., Domingo-Ferrer, J.: Machine learning explainability through comprehensible decision trees. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2019. LNCS, vol. 11713, pp. 15\u201326. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29726-8_2"},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Ribeiro, M., Singh, S., Guestrin, C.: Why should i trust you?: explaining the predictions of any classifier. In: Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, San Diego, CA (2016)","DOI":"10.18653\/v1\/N16-3020"},{"key":"1_CR36","unstructured":"Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems (2017)"},{"issue":"7","key":"1_CR37","doi-asserted-by":"publisher","first-page":"0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), 0130140 (2015)","journal-title":"PLoS ONE"},{"key":"1_CR38","unstructured":"Alber, M., et al.: iNNvestigate neural networks!, arXiv (2018)"},{"key":"1_CR39","unstructured":"Kindermans, P.-J., et al.: Learning how to explain neural networks: patternnet and patternattribution (2017)"},{"key":"1_CR40","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-030-28954-6_10","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"G Montavon","year":"2019","unstructured":"Montavon, G., Binder, A., Lapuschkin, S., Samek, W., M\u00fcller, K.-R.: Layer-wise relevance propagation: an overview. 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. 193\u2013209. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_10"},{"key":"1_CR41","unstructured":"https:\/\/github.com\/oracle\/Skater. Accessed 30 Dec 2020"},{"key":"1_CR42","doi-asserted-by":"crossref","unstructured":"Gurumoorthy, K.S., Dhurandhar, A., Cecchi, G., Aggarwal, C.: Efficient data representation by selecting prototypes with importance weights. In: ICD. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00036"},{"key":"1_CR43","unstructured":"Dhurandhar, A., et al.: Explanations based on the missing: towards contrastive explanations with pertinent negatives. In: Advances in Neural Information Processing Systems (2018)"},{"key":"1_CR44","unstructured":"https:\/\/flowcast.ai. Accessed 30 Dec 2020"},{"key":"1_CR45","unstructured":"https:\/\/resources.flowcast.ai\/resources\/big-data-smart-credit-white-paper\/. Accessed 18 Mar 2021"},{"key":"1_CR46","doi-asserted-by":"crossref","unstructured":"Szczepa\u0144ski, M., Chora\u015b, M., Pawlicki, M., Kozik, R.: Achieving explainability of intrusion detection system by hybrid oracle-explainer approach. In: IJCNN (2020)","DOI":"10.1109\/IJCNN48605.2020.9207199"},{"key":"1_CR47","unstructured":"https:\/\/darwinai.com. Accessed 30 Dec 2020"},{"key":"1_CR48","unstructured":"https:\/\/www.fiddler.ai. Accessed 30 Dec 2020"},{"key":"1_CR49","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks, arXiv preprint arXiv:1703.01365 (2017)"},{"key":"1_CR50","unstructured":"Maleki, S., Tran-Thanh, L., Hines, G., Rahwan, T., Rogers, A.: Bounding the estimation error of sampling-based Shapley value approximation, arXiv:1306.4265 (2013)"},{"key":"1_CR51","doi-asserted-by":"crossref","unstructured":"Kapishnikov, A., Bolukbasi, T., Vi\u00e9gas, F., Terry, M.: Xrai: better attributions through regions. In: IEEE International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00505"},{"key":"1_CR52","unstructured":"https:\/\/www.rulex.ai. Accessed 30 Dec 2020"},{"key":"1_CR53","unstructured":"https:\/\/kyndi.com. Accessed 30 Dec 2020"},{"key":"1_CR54","unstructured":"https:\/\/www.h2o.ai. Accessed 30 Dec 2020"},{"key":"1_CR55","unstructured":"https:\/\/www.ventureradar.com. Accessed 30 Dec 2020"},{"key":"1_CR56","unstructured":"https:\/\/www.sparta.eu\/programs\/safair\/. Accessed 18 Mar 2021"},{"key":"1_CR57","unstructured":"https:\/\/cordis.europa.eu\/project\/id\/952060. Accessed 30 Dec 2020"},{"key":"1_CR58","doi-asserted-by":"crossref","unstructured":"Zanni-Merk, C.: On the Need of an Explainable Artificial Intelligence (2020)","DOI":"10.1007\/978-3-030-30440-9_1"},{"issue":"1","key":"1_CR59","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/BF00116900","volume":"23","author":"G Widmer","year":"1996","unstructured":"Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69\u2013101 (1996)","journal-title":"Mach. Learn."},{"key":"1_CR60","unstructured":"https:\/\/peterasaro.org\/writing\/Asaro_PredictivePolicing.pdf. Accessed 30 Dec 2020"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77970-2_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T22:02:21Z","timestamp":1749506541000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77970-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030779696","9783030779702"],"references-count":60,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77970-2_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krakow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2021\/","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":"156","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":"48","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":"14","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":"31% - 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":"2.8","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":"3.9","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"212 full and 43 short papers were selected from 479 submissions to the workshops\/ thematic tracks. The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}