{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:19:26Z","timestamp":1773249566355,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819947515","type":"print"},{"value":"9789819947522","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\/978-981-99-4752-2_26","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T16:02:10Z","timestamp":1690732930000},"page":"310-318","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Explainable Artificial Intelligence 101: Techniques, Applications and Challenges"],"prefix":"10.1007","author":[{"given":"Wiktor","family":"Kurek","sequence":"first","affiliation":[]},{"given":"Marek","family":"Pawlicki","sequence":"additional","affiliation":[]},{"given":"Aleksandra","family":"Pawlicka","sequence":"additional","affiliation":[]},{"given":"Rafa\u0142","family":"Kozik","sequence":"additional","affiliation":[]},{"given":"Micha\u0142","family":"Chora\u015b","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"issue":"1","key":"26_CR1","first-page":"451","volume":"72","author":"A Ambhaikar","year":"2023","unstructured":"Ambhaikar, A.: A survey on health care and expert system. Math. Statist. Eng. Appl. 72(1), 451\u2013461 (2023)","journal-title":"Math. Statist. Eng. Appl."},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Bahani, K., Moujabbir, M., Ramdani, M.: An accurate fuzzy rule-based classification systems for heart disease diagnosis. Sci. African 14,\u00a0e01019 (2021)","DOI":"10.1016\/j.sciaf.2021.e01019"},{"key":"26_CR3","first-page":"1","volume":"18","author":"AG Baydin","year":"2018","unstructured":"Baydin, A.G., Pearlmutter, B.A., Radul, A.A., Siskind, J.M.: Automatic differentiation in machine learning: a survey. J. Mach. Learn. Res. 18, 1\u201343 (2018)","journal-title":"J. Mach. Learn. Res."},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Burkhardt, S., Brugger, J., Wagner, N., Ahmadi, Z., Kersting, K., Kramer, S.: Rule extraction from binary neural networks with convolutional rules for model validation. Front. Artif. Intell.\u00a04, 642263 (2021)","DOI":"10.3389\/frai.2021.642263"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Cambra Baseca, C., Sendra, S., Lloret, J., Tomas, J.: A smart decision system for digital farming. Agronomy 9(5), 216 (2019)","DOI":"10.3390\/agronomy9050216"},{"key":"26_CR6","doi-asserted-by":"publisher","unstructured":"Chora\u015b, M., Pawlicki, M., Puchalski, D., Kozik, R.: Machine learning\u2013the results are not the only thing that matters! what about security, explainability and fair- ness?\u00a0In: Krzhizhanovskaya, V.V. et al (eds.). Computational Science \u2013 ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol. 12140, pp. 615\u2013628. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50423-6_46","DOI":"10.1007\/978-3-030-50423-6_46"},{"issue":"10","key":"26_CR7","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2347736.2347755","volume":"55","author":"P Domingos","year":"2012","unstructured":"Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78\u201387 (2012)","journal-title":"Commun. ACM"},{"key":"26_CR8","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Dwivedi, R., et al.: Explainable ai (xai): Core ideas, techniques, and solutions. ACM Comput. Surv. 55(9), 1\u201333 (2023)","DOI":"10.1145\/3561048"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Friedman, J.H., Popescu, B.E.: Predictive learning via rule ensembles. Annal. Appl. Statist. 2, 916\u2013954 (2008)","DOI":"10.1214\/07-AOAS148"},{"key":"26_CR11","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT press (2016)"},{"issue":"5","key":"26_CR12","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 computing surveys (CSUR) 51(5), 1\u201342 (2018)","journal-title":"ACM computing surveys (CSUR)"},{"key":"26_CR13","unstructured":"Han, J., Kamber, M., Pei, J.: Data mining concepts and techniques third edition. University of Illinois at Urbana-Champaign Micheline Kamber Jian Pei Simon Fraser University (2012)"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Henderson, J., et al.: Certifai: a toolkit for building trust in AI systems. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 5249\u20135251 (2021)","DOI":"10.24963\/ijcai.2020\/759"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Liao, Q.V., Gruen, D., Miller, S.: Questioning the AI: informing design practices for explainable AI user experiences. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1\u201315 (2020)","DOI":"10.1145\/3313831.3376590"},{"key":"26_CR16","unstructured":"Liao, Q.V., Varshney, K.R.: Human-centered explainable ai (xai): From algorithms to user experiences. arXiv preprint arXiv:2110.10790 (2021)"},{"issue":"3","key":"26_CR17","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton, Z.C.: The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31\u201357 (2018)","journal-title":"Queue"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Luo, C., et al.: Rulefit-based nomogram using inflammatory indicators for predicting survival in nasopharyngeal carcinoma, a bi-center study. J. Inflamm. Res. 15, 4803\u20134815 (2022)","DOI":"10.2147\/JIR.S366922"},{"key":"26_CR19","first-page":"1","volume":"2021","author":"B Mahbooba","year":"2021","unstructured":"Mahbooba, B., Timilsina, M., Sahal, R., Serrano, M.: Explainable artificial intelligence (xai) to enhance trust management in intrusion detection systems using decision tree model. Complexity 2021, 1\u201311 (2021)","journal-title":"Complexity"},{"key":"26_CR20","unstructured":"Mitrovi\u0107, S., Andreoletti, D., Ayoub, O.: Chatgpt or human? detect and explain. Explaining Decisions of Machine Learning Model for Detecting Short Chatgpt- Generated Text. arXiv preprint arXiv:2301.13852 (2023)"},{"key":"26_CR21","unstructured":"Molnar, C.: Interpretable machine learning. Lulu.com (2020)"},{"key":"26_CR22","unstructured":"Nalepa, G., Araszkiewicz, M., Nowaczyk, S., Bobek, S.: Building trust to AI systems through explainability: technical and legal perspectives (2019)"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Nwakanma, C.I., et al.: Explainable artificial intelligence (xai) for intrusion detection and mitigation in intelligent connected vehicles: a review. Appl. Sci. 13(3), 1252 (2023)","DOI":"10.3390\/app13031252"},{"key":"26_CR24","doi-asserted-by":"publisher","unstructured":"Panesar, A.: Machine learning and AI for healthcare. Springer (2019). https:\/\/doi.org\/10.1007\/978-1-4842-3799-1","DOI":"10.1007\/978-1-4842-3799-1"},{"key":"26_CR25","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan, J.R.: Induction of decision trees. Machine learning 1, 81\u2013106 (1986)","journal-title":"Induction of decision trees. Machine learning"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Reddy, B., Fields, R.: From past to present: a comprehensive technical review of rule-based expert systems from 1980\u20132021. In: Proceedings of the 2022 ACMSoutheast Conference, pp. 167\u2013172 (2022)","DOI":"10.1145\/3476883.3520211"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.:\u00a0\u201cWhy should I trust you?\u201d explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"26_CR28","doi-asserted-by":"crossref","unstructured":"Roth, A.M., Liang, J., Manocha, D.: Xai-n: Sensor-based robot navigation using expert policies and decision trees. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2053\u20132060. IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9636759"},{"key":"26_CR29","unstructured":"Samek, W., Wiegand, T., M\u00fcller, K.R.: Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296 (2017)"},{"key":"26_CR30","doi-asserted-by":"crossref","unstructured":"Schaaf, N., Huber, M., Maucher, J.: Enhancing decision tree based interpretation of deep neural networks through l1-orthogonal regularization. In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pp. 42\u201349. IEEE (2019)","DOI":"10.1109\/ICMLA.2019.00016"},{"key":"26_CR31","doi-asserted-by":"crossref","unstructured":"Sharma, S., Henderson, J., Ghosh, J.: Certifai: counterfactual explanations for robustness, transparency, interpretability, and fairness of artificial intelligence models. arXiv preprint arXiv:1905.07857 (2019)","DOI":"10.1145\/3375627.3375812"},{"key":"26_CR32","doi-asserted-by":"publisher","unstructured":"Szczepa\u0144ski, M., Chora\u015b, M., Pawlicki, M., Pawlicka, A.: The methods and approaches of explainable artificial intelligence.\u00a0In: Paszynski, M., Kranzlm\u00fcller, D., Krzhizhanovskaya, V.V., Dongarra, \nJ.J., Sloot, P.M. (eds.) Computational Science \u2013 ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol. 12745, pp. 3\u201317. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77970-2_1","DOI":"10.1007\/978-3-030-77970-2_1"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Szczepa\u0144ski, M., Pawlicki, M., Kozik, R., Chora\u015b, M.: New explainability method for bert-based model in fake news detection. Sci. Rep. 11(1), 23705 (2021)","DOI":"10.1038\/s41598-021-03100-6"},{"key":"26_CR34","doi-asserted-by":"crossref","unstructured":"Van der Velden, B.H., Kuijf, H.J., Gilhuijs, K.G., Viergever, M.A.: Explainable artificial intelligence (xai) in deep learning-based medical image analysis. Med. Image Anal. 79, 102470 (2022)","DOI":"10.1016\/j.media.2022.102470"},{"issue":"5","key":"26_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3527448","volume":"55","author":"GA Vouros","year":"2022","unstructured":"Vouros, G.A.: Explainable deep reinforcement learning: state of the art and challenges. ACM Comput. Surv. 55(5), 1\u201339 (2022)","journal-title":"ACM Comput. Surv."},{"key":"26_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Hamadi, H.A., Damiani, E., Yeun, C.Y., Taher, F.: Explainable artificial intelligence applications in cyber security: State-of-the-art in research. arXiv preprint arXiv:2208.14937 (2022)","DOI":"10.1109\/ACCESS.2022.3204051"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4752-2_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T23:10:20Z","timestamp":1690931420000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4752-2_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947515","9789819947522"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4752-2_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}