{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T11:55:54Z","timestamp":1784548554194,"version":"3.55.0"},"reference-count":257,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62250710682"],"award-info":[{"award-number":["62250710682"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangdong Provincial Key Laboratory","award":["2020B121201001"],"award-info":[{"award-number":["2020B121201001"]}]},{"name":"Program for Guangdong Introducing Innovative and Entrepreneurial Teams","award":["2017ZT07X386"],"award-info":[{"award-number":["2017ZT07X386"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Auton. Adapt. Syst."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>Explainable artificial intelligence (XAI) has gained significant attention, especially in AI-powered autonomous and adaptive systems (AASs). However, a discernible disconnect exists among research efforts across different communities. The machine learning community often overlooks \u201cexplaining to whom,\u201d while the human-computer interaction community has examined various stakeholders with diverse explanation needs without addressing which XAI methods meet these requirements. Currently, no clear guidance exists on which XAI methods suit which specific stakeholders and their distinct needs. This hinders the achievement of the goal of XAI: providing human users with understandable interpretations. To bridge this gap, this article presents a comprehensive XAI roadmap. Based on an extensive literature review, the roadmap summarizes different stakeholders, their explanation needs at different stages of the AI system lifecycle, the questions they may pose, and existing XAI methods. Then, by utilizing stakeholders\u2019 inquiries as a conduit, the roadmap connects their needs to prevailing XAI methods, providing a guideline to assist researchers and practitioners to determine more easily which XAI methodologies can meet the specific needs of stakeholders in AASs. Finally, the roadmap discusses the limitations of existing XAI methods and outlines directions for future research.<\/jats:p>","DOI":"10.1145\/3702004","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T12:50:34Z","timestamp":1730811034000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3118-8742","authenticated-orcid":false,"given":"Ziming","family":"Wang","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3685-2822","authenticated-orcid":false,"given":"Changwu","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8837-4442","authenticated-orcid":false,"given":"Xin","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Data Science, Lingnan University, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,24]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2022.3188750"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1116-3"},{"issue":"1","key":"e_1_3_1_5_2","first-page":"1086","article-title":"Fairsight: Visual analytics for fairness in decision making","volume":"26","author":"Ahn Yongsu","year":"2019","unstructured":"Yongsu Ahn and Yu-Ru Lin. 2019. Fairsight: Visual analytics for fairness in decision making. IEEE Transactions on Visualization and Computer Graphics 26, 1 (2019), 1086\u20131095.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_3_1_6_2","first-page":"7786","article-title":"Towards robust interpretability with self-explaining neural networks","volume":"31","author":"Melis David Alvarez","year":"2018","unstructured":"David Alvarez Melis and Tommi Jaakkola. 2018. Towards robust interpretability with self-explaining neural networks. In Advances in Neural Information Processing Systems, Vol. 31, 7786\u20137795.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-SEIP.2019.00042"},{"key":"e_1_3_1_8_2","unstructured":"Dario Amodei Chris Olah Jacob Steinhardt Paul Christiano John Schulman and Dan Man\u00e9. 2016. Concrete problems in AI safety. arXiv:1606.06565. Retrieved from http:\/\/arxiv.org\/abs\/1606.06565"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/app11115088"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12377"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"e_1_3_1_12_2","unstructured":"Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind Samuel C. Hoffman Stephanie Houde Q. Vera Liao Ronny Luss Aleksandra Mojsilovi\u0107 et al. 2019. One explanation does not fit all: A toolkit and taxonomy of AI explainability techniques. arXiv:1909.03012. Retrieved from http:\/\/arxiv.org\/abs\/1909.03012"},{"key":"e_1_3_1_13_2","first-page":"130","article-title":"AI explainability 360: An extensible toolkit for understanding data and machine learning models","volume":"21","author":"Arya Vijay","year":"2020","unstructured":"Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, et al. 2020. AI explainability 360: An extensible toolkit for understanding data and machine learning models. Journal of Machine Learning Research 21, 130 (2020), 1\u20136.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1859912"},{"key":"e_1_3_1_15_2","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations, 1\u201311."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45855.2022.9838766"},{"key":"e_1_3_1_17_2","unstructured":"John Battelle. 2013. Behind the banner a visualization of the adtech ecosystem. Retrieved from https:\/\/battellemedia.com\/archives\/2013\/05\/behind-the-banner-a-visualization-of-the-adtech-ecosystem"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.354"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2021.688969"},{"key":"e_1_3_1_20_2","first-page":"1","article-title":"Towards explainable neural-symbolic visual reasoning","author":"Bennetot Adrien","year":"2019","unstructured":"Adrien Bennetot, Jean-Luc Laurent, Raja Chatila, and Natalia D\u00edaz-Rodr\u00edguez. 2019. Towards explainable neural-symbolic visual reasoning. In Proceedings of the IJCAI Neural-Symbolic Learning and Reasoning Workshop, 1\u20136. Retrieved from http:\/\/arxiv.org\/abs\/1909.09065","journal-title":"Proceedings of the IJCAI Neural-Symbolic Learning and Reasoning Workshop"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44163-021-00008-y"},{"key":"e_1_3_1_22_2","first-page":"3016","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence","author":"Bhatt Umang","year":"2021","unstructured":"Umang Bhatt, Adrian Weller, and Jos\u00e9 M. F. Moura. 2021. Evaluating and aggregating feature-based model explanations. In Proceedings of the 29th International Joint Conference on Artificial Intelligence, 3016\u20133022."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3375624"},{"key":"e_1_3_1_24_2","unstructured":"P. Biecek. 2019. Ceteris paribus plots (what-if plots) for explanations of a single observation. Retrieved from https:\/\/github.com\/pbiecek\/ceterisParibus"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2022.107617"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8461053"},{"key":"e_1_3_1_27_2","unstructured":"Mariusz Bojarski Philip Yeres Anna Choromanska Krzysztof Choromanski Bernhard Firner Lawrence Jackel and Urs Muller. 2017. Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv:1704.07911. Retrieved from http:\/\/arxiv.org\/abs\/1704.07911"},{"key":"e_1_3_1_28_2","first-page":"1","article-title":"Concept-level debugging of part-prototype networks","author":"Bontempelli Andrea","year":"2022","unstructured":"Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, and Andrea Passerini. 2022. Concept-level debugging of part-prototype networks. In Proceedings of the Workshop on Trustworthy Artificial Intelligence as a Part of the ECML\/PKDD 22 Program, 1\u201313","journal-title":"Proceedings of the Workshop on Trustworthy Artificial Intelligence as a Part of the ECML\/PKDD 22 Program"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775113"},{"issue":"1","key":"e_1_3_1_30_2","first-page":"56","article-title":"Explaining plans at scale: Scalable path planning explanations in navigation meshes using inverse optimization","volume":"31","author":"Brandao Martim","year":"2020","unstructured":"Martim Brandao and Daniele Magazzeni. 2020. Explaining plans at scale: Scalable path planning explanations in navigation meshes using inverse optimization. In Proceedings of the International Conference on Automated Planning and Scheduling, 31, 1 (2020), 56\u201364.","journal-title":"Proceedings of the International Conference on Automated Planning and Scheduling"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3334480.3383047"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3502289"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88483-3_24"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00766-020-00333-1"},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","unstructured":"Ching-Ju Chen Ling-Wei Chen Chun-Hao Yang Ya-Yu Huang and Yueh-Min Huang. 2021. Improving CNN-based pest recognition with a post-hoc explanation of XAI. Retrieved from http:\/\/www.researchsquare.com\/article\/rs-782408\/v1","DOI":"10.21203\/rs.3.rs-782408\/v1"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM52615.2021.9669648"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-015-9434-x"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz769"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1148\/rg.220105"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117945"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tele.2024.102135"},{"key":"e_1_3_1_42_2","unstructured":"European Commission. 2019. Ethics guidelines for trustworthy AI. Retrieved from https:\/\/digital-strategy.ec.europa.eu\/en\/library\/ethics-guidelines-trustworthy-ai"},{"key":"e_1_3_1_43_2","first-page":"24","article-title":"Extracting tree-structured representations of trained networks","volume":"8","author":"Craven Mark","year":"1995","unstructured":"Mark Craven and Jude Shavlik. 1995. Extracting tree-structured representations of trained networks. In Advances in Neural Information Processing Systems, Vol. 8, 24\u201330.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-335-6.50013-1"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101982"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.42"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.11.018"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3461778.3462131"},{"key":"e_1_3_1_49_2","first-page":"592","article-title":"Explanations based on the missing: Towards contrastive explanations with pertinent negatives","volume":"31","author":"Dhurandhar Amit","year":"2018","unstructured":"Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. In Advances in Neural Information Processing Systems, Vol. 31, 592\u2013603.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/2347736.2347755"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2023.104358"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.110"},{"key":"e_1_3_1_53_2","unstructured":"Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv:1702.08608. Retrieved from http:\/\/arxiv.org\/abs\/1702.08608"},{"key":"e_1_3_1_54_2","doi-asserted-by":"crossref","unstructured":"Finale Doshi-Velez Mason Kortz Ryan Budish Chris Bavitz Sam Gershman David O\u2019Brien Kate Scott Stuart Schieber James Waldo David Weinberger et al. 2017. Accountability of AI under the law: The role of explanation. arXiv:1711.01134. Retrieved from http:\/\/arxiv.org\/abs\/1711.01134","DOI":"10.2139\/ssrn.3064761"},{"key":"e_1_3_1_55_2","first-page":"1","article-title":"Explainable artificial intelligence (XAI) for increasing user trust in deep reinforcement learning driven autonomous systems","author":"Druce Jeff","year":"2019","unstructured":"Jeff Druce, Michael Harradon, and James Tittle. 2019. Explainable artificial intelligence (XAI) for increasing user trust in deep reinforcement learning driven autonomous systems. In Proceedings of the NeurIPS 2019 Deep RL Workshop, 1\u20139. Retrieved from http:\/\/arxiv.org\/abs\/2106.03775","journal-title":"Proceedings of the NeurIPS 2019 Deep RL Workshop"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCNT49239.2020.9225621"},{"key":"e_1_3_1_57_2","first-page":"10s","article-title":"The elements of end-to-end deep face recognition: A survey of recent advances","volume":"54","author":"Du Hang","year":"2020","unstructured":"Hang Du, Hailin Shi, Dan Zeng, Xiao-Ping Zhang, and Tao Mei. 2020. The elements of end-to-end deep face recognition: A survey of recent advances. ACM Computing Surveys 54, 10s (2020), 1\u201342.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445188"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411763.3441342"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-82098-3"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1177\/0018720816681350"},{"key":"e_1_3_1_62_2","first-page":"1","article-title":"Visualizing higher-layer features of a deep network","volume":"3","author":"Erhan Dumitru","year":"2009","unstructured":"Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2009. Visualizing higher-layer features of a deep network. University of Montreal 1341, 3 (2009), 1\u201313.","journal-title":"University of Montreal"},{"key":"e_1_3_1_63_2","volume-title":"Understanding Representations Learned in Deep Architectures","author":"Erhan Dumitru","year":"2010","unstructured":"Dumitru Erhan, Aaron Courville, and Yoshua Bengio. 2010. Understanding Representations Learned in Deep Architectures. Technical Report 1355, Department dInformatique et Recherche Operationnelle, University of Montreal, QC, Canada."},{"key":"e_1_3_1_64_2","unstructured":"Gary Ericson William Anton Rohm Jos\u00e9e Martens Kent Sharkey Craig Casey Beth Harvey and Nick Schonning. 2017. Team data science process documentation. Retrieved 11 April 2017 from. http:\/\/learn.microsoft.com\/en-us\/azure\/architecture\/data-science-process\/overview"},{"key":"e_1_3_1_65_2","unstructured":"Magnus Falk. 2019. Artificial intelligence in the boardroom. Retrieved from https:\/\/www.fca.org.uk\/insight\/artificial-intelligence-boardroom"},{"key":"e_1_3_1_66_2","unstructured":"Fan Fang Carmine Ventre Lingbo Li Leslie Kanthan Fan Wu and Michail Basios. 2020. Better model selection with a new definition of feature importance. arXiv:2009.07708. Retrieved from http:\/\/arxiv.org\/abs\/2009.07708"},{"key":"e_1_3_1_67_2","unstructured":"Juliana Jansen Ferreira and Mateus Monteiro. 2021. The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions. arXiv:2102.05460. Retrieved from http:\/\/arxiv.org\/abs\/2102.05460"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-49760-6_4"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00612-w"},{"key":"e_1_3_1_71_2","unstructured":"Detlev Gabel and Tim Hickman. 2019. GDPR handbook: Unlocking the EU general data protection regulation. White & Case Technology Newsflash (2019). Retrieved from https:\/\/www.whitecase.com\/insight-our-thinking\/gdpr-handbook-unlocking-eu-general-data-protection-regulation"},{"issue":"2","key":"e_1_3_1_72_2","first-page":"1351","article-title":"Cross-platform item recommendation for online social e-commerce","volume":"35","author":"Gao Chen","year":"2023","unstructured":"Chen Gao, Tzu-Heng Lin, Nian Li, Depeng Jin, and Yong Li. 2023. Cross-platform item recommendation for online social e-commerce. IEEE Transactions on Knowledge and Data Engineering 35, 2 (2023), 1351\u20131364.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-83620-7_7"},{"key":"e_1_3_1_74_2","doi-asserted-by":"crossref","unstructured":"Julie Gerlings Arisa Shollo and Ioanna Constantiou. 2020. Reviewing the need for explainable artificial intelligence (XAI). arXiv:2012.01007. Retrieved from http:\/\/arxiv.org\/abs\/2012.01007","DOI":"10.24251\/HICSS.2021.156"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2018.00018"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2014.907095"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533236"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-70378-2_20"},{"issue":"1","key":"e_1_3_1_79_2","first-page":"1","article-title":"Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations","volume":"10","author":"Gudivada Venkat","year":"2017","unstructured":"Venkat Gudivada, Amy Apon, and Junhua Ding. 2017. Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations. International Journal on Advances in Software 10, 1 (2017), 1\u201320.","journal-title":"International Journal on Advances in Software"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2019.2957223"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"key":"e_1_3_1_82_2","unstructured":"Calvin Guillot Suarez. 2022. Human-in-the-Loop Hyperparameter Tuning of Deep Nets to Improve Explainability of Classifications. Master's thesis. Aalto University. School of Electrical Engineering. Retrieved from http:\/\/urn.fi\/URN:NBN:fi:aalto-202205223354"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00036"},{"key":"e_1_3_1_84_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-021-09993-1"},{"key":"e_1_3_1_85_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.nlpcovid19-2.36"},{"key":"e_1_3_1_86_2","unstructured":"Michael Harradon Jeff Druce and Brian Ruttenberg. 2018. Causal learning and explanation of deep neural networks via autoencoded activations. arXiv:1802.00541. Retrieved from http:\/\/arxiv.org\/abs\/1802.00541"},{"key":"e_1_3_1_87_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.3043603"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_1"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0368-8"},{"key":"e_1_3_1_90_2","unstructured":"Robert R. Hoffman Shane T. Mueller Gary Klein and Jordan Litman. 2018. Metrics for explainable AI: Challenges and prospects. arXiv:1812.04608. Retrieved from http:\/\/arxiv.org\/abs\/1812.04608"},{"key":"e_1_3_1_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2934659"},{"key":"e_1_3_1_92_2","unstructured":"Andreas Holzinger Chris Biemann Constantinos S. Pattichis and Douglas B. Kell. 2017. What do we need to build explainable AI systems for the medical domain? arXiv:1712.09923. Retrieved from http:\/\/arxiv.org\/abs\/1712.09923"},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3194503"},{"key":"e_1_3_1_94_2","doi-asserted-by":"publisher","DOI":"10.1145\/3665647"},{"key":"e_1_3_1_95_2","unstructured":"Fatima Hussain Rasheed Hussain and Ekram Hossain. 2021. Explainable artificial intelligence (XAI): An engineering perspective. arXiv:2101.03613. Retrieved from http:\/\/arxiv.org\/abs\/2101.03613"},{"key":"e_1_3_1_96_2","first-page":"541","volume-title":"Proceedings of the 2nd Southern African Conference for Artificial Intelligence Research","author":"Jafta Gandhi","year":"2022","unstructured":"Gandhi Jafta, Alta de Waal, Iena Derks, and Emma Ruttkamp-Bloem. 2022. Evaluation of XAI as an enabler for fairness, accountability and transparency. In Proceedings of the 2nd Southern African Conference for Artificial Intelligence Research, 541\u2013542."},{"key":"e_1_3_1_97_2","unstructured":"Helen Jiang and Erwen Senge. 2021. On two XAI cultures: A case study of non-technical explanations in deployed AI system. arXiv:2112.01016. Retrieved from http:\/\/arxiv.org\/abs\/2112.01016"},{"key":"e_1_3_1_98_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0088-2"},{"key":"e_1_3_1_99_2","first-page":"13","volume-title":"Proceedings of the 13th International Conference on Artificial Neural Networks","author":"Johansson U.","year":"2003","unstructured":"U. Johansson, R. K\u00f6nig, and L. Niklasson. 2003. Rule extraction from trained neural networks using genetic programming. In Proceedings of the 13th International Conference on Artificial Neural Networks, 13\u201316."},{"key":"e_1_3_1_100_2","doi-asserted-by":"publisher","DOI":"10.1109\/CIDM.2009.4938655"},{"key":"e_1_3_1_101_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jiph.2020.02.042"},{"key":"e_1_3_1_102_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744718"},{"key":"e_1_3_1_103_2","volume-title":"Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning","author":"Kamath Uday","year":"2021","unstructured":"Uday Kamath and John Liu. 2021. Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning. Springer."},{"key":"e_1_3_1_104_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68780-9_53"},{"key":"e_1_3_1_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102681"},{"key":"e_1_3_1_106_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-1476-8"},{"key":"e_1_3_1_107_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3420415"},{"key":"e_1_3_1_108_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_35"},{"key":"e_1_3_1_109_2","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884783"},{"key":"e_1_3_1_110_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1699-3_13"},{"key":"e_1_3_1_111_2","first-page":"185","article-title":"Exploratory data analysis","author":"Komorowski Matthieu","year":"2016","unstructured":"Matthieu Komorowski, Dominic C. Marshall, Justin D. Salciccioli, and Yves Crutain. 2016. Exploratory data analysis. Springer International Publishing, Cham, 185\u2013203.","journal-title":"Springer International Publishing, Cham"},{"key":"e_1_3_1_112_2","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858529"},{"key":"e_1_3_1_113_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(98)00181-2"},{"key":"e_1_3_1_114_2","first-page":"1","article-title":"Evolving complex yet interpretable representations: Application to Alzheimer's diagnosis and prognosis","author":"Kr\u00f6ll Jean-Philippe","year":"2020","unstructured":"Jean-Philippe Kr\u00f6ll, Simon B. Eickhoff, Felix Hoffstaedter, and Kaustubh R. Patil. 2020. Evolving complex yet interpretable representations: Application to Alzheimer's diagnosis and prognosis. In Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC \u201920). IEEE, 1\u20138.","journal-title":"Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC \u201920)"},{"key":"e_1_3_1_115_2","doi-asserted-by":"publisher","DOI":"10.1145\/2678025.2701399"},{"key":"e_1_3_1_116_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103473"},{"key":"e_1_3_1_117_2","unstructured":"Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Xavier Renard and Marcin Detyniecki. 2017. Inverse classification for comparison-based interpretability in machine learning. arXiv:1712.08443. Retrieved from http:\/\/arxiv.org\/abs\/1712.08443"},{"key":"e_1_3_1_118_2","doi-asserted-by":"crossref","unstructured":"Tao Lei Regina Barzilay and Tommi Jaakkola. 2016. Rationalizing neural predictions. arXiv:1606.04155. Retrieved from http:\/\/arxiv.org\/abs\/1606.04155","DOI":"10.18653\/v1\/D16-1011"},{"key":"e_1_3_1_119_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13347-017-0279-x"},{"key":"e_1_3_1_120_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11771"},{"key":"e_1_3_1_121_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376590"},{"key":"e_1_3_1_122_2","unstructured":"Q. Vera Liao and Kush R. Varshney. 2021. Human-centered explainable AI (XAI): From algorithms to user experiences. arXiv:2110.10790. Retrieved from http:\/\/arxiv.org\/abs\/2110.10790"},{"key":"e_1_3_1_123_2","doi-asserted-by":"publisher","DOI":"10.1145\/1864349.1864353"},{"key":"e_1_3_1_124_2","doi-asserted-by":"publisher","DOI":"10.1145\/1518701.1519023"},{"key":"e_1_3_1_125_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3534628"},{"key":"e_1_3_1_126_2","doi-asserted-by":"publisher","DOI":"10.3390\/e23010018"},{"key":"e_1_3_1_127_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744938"},{"key":"e_1_3_1_128_2","doi-asserted-by":"publisher","DOI":"10.3390\/biomedinformatics2010001"},{"key":"e_1_3_1_129_2","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339556"},{"key":"e_1_3_1_130_2","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487579"},{"key":"e_1_3_1_131_2","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg Scott M.","year":"2017","unstructured":"Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 4768\u20134777.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_132_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0304-0"},{"key":"e_1_3_1_133_2","doi-asserted-by":"crossref","unstructured":"Minh-Thang Luong Hieu Pham and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv:1508.04025. Retrieved from http:\/\/arxiv.org\/abs\/1508.04025","DOI":"10.18653\/v1\/D15-1166"},{"key":"e_1_3_1_134_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-51924-7_8"},{"key":"e_1_3_1_135_2","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468547"},{"key":"e_1_3_1_136_2","doi-asserted-by":"publisher","DOI":"10.1109\/SIBGRAPI51738.2020.00053"},{"key":"e_1_3_1_137_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-194515"},{"key":"e_1_3_1_138_2","doi-asserted-by":"publisher","DOI":"10.1145\/2745390"},{"key":"e_1_3_1_139_2","first-page":"2207","article-title":"Artificial intelligence explainability: The technical and ethical dimensions","volume":"379","author":"McDermid John A.","year":"2021","unstructured":"John A. McDermid, Yan Jia, Zoe Porter, and Ibrahim Habli. 2021. Artificial intelligence explainability: The technical and ethical dimensions. Philosophical Transactions of the Royal Society A 379, 2207 (2021), 20200363.","journal-title":"Philosophical Transactions of the Royal Society A"},{"key":"e_1_3_1_140_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14525"},{"key":"e_1_3_1_141_2","doi-asserted-by":"publisher","DOI":"10.1080\/10580530.2020.1849465"},{"key":"e_1_3_1_142_2","doi-asserted-by":"publisher","DOI":"10.2478\/jaiscr-2021-0004"},{"key":"e_1_3_1_143_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"e_1_3_1_144_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2864812"},{"key":"e_1_3_1_145_2","doi-asserted-by":"publisher","DOI":"10.1177\/2053951716679679"},{"key":"e_1_3_1_146_2","doi-asserted-by":"publisher","DOI":"10.1145\/3387166"},{"key":"e_1_3_1_147_2","volume-title":"Interpretable Machine Learning","author":"Molnar Christoph","year":"2020","unstructured":"Christoph Molnar. 2020. Interpretable Machine Learning. Lulu. com."},{"key":"e_1_3_1_148_2","first-page":"104","volume-title":"Proceedings of the 2nd Workshop on Explainable Artificial Intelligence","author":"Monteath Isaac","year":"2018","unstructured":"Isaac Monteath and Raymond Sheh. 2018. Assisted and incremental medical diagnosis using explainable artificial intelligence. In Proceedings of the 2nd Workshop on Explainable Artificial Intelligence, 104\u2013108."},{"key":"e_1_3_1_149_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI52183.2021.00100"},{"key":"e_1_3_1_150_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.socialnlp-1.8"},{"key":"e_1_3_1_151_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00157"},{"key":"e_1_3_1_152_2","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372850"},{"key":"e_1_3_1_153_2","volume-title":"IterSHAP: An XAI Feature Selection Method for Small High-Dimensional Datasets","author":"Mourik F. G.","year":"2023","unstructured":"F. G. Mourik. 2023. IterSHAP: An XAI Feature Selection Method for Small High-Dimensional Datasets. Master's thesis. University of Twente."},{"key":"e_1_3_1_154_2","doi-asserted-by":"publisher","DOI":"10.1177\/1064804620920870"},{"key":"e_1_3_1_155_2","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172240"},{"key":"e_1_3_1_156_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397481.3450658"},{"key":"e_1_3_1_157_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678840"},{"key":"e_1_3_1_158_2","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00010"},{"key":"e_1_3_1_159_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-3800(02)00064-9"},{"key":"e_1_3_1_160_2","unstructured":"Lara O\u2019Reilly. 2014. Here's one way to find out which advertisers are tracking you across the internet. Retrieved from https:\/\/www.businessinsider.com\/floodwatch-ad-tracking-chrome-extension-2014-10"},{"issue":"3","key":"e_1_3_1_161_2","first-page":"91","article-title":"Interpretable machine learning model selection for breast cancer diagnosis based on k-means clustering","volume":"43","author":"Ouedraogo Dieudonne N.","year":"2021","unstructured":"Dieudonne N. Ouedraogo. 2021. Interpretable machine learning model selection for breast cancer diagnosis based on k-means clustering. Applied Medical Informatics 43, 3 (2021), 91\u2013102.","journal-title":"Applied Medical Informatics"},{"key":"e_1_3_1_162_2","first-page":"41","article-title":"Scientific explanation and computation","author":"Overton James","year":"2011","unstructured":"James Overton. 2011. Scientific explanation and computation. In ExaCt, 41\u201350.","journal-title":"ExaCt"},{"key":"e_1_3_1_163_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43895-0_56"},{"key":"e_1_3_1_164_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21165657"},{"key":"e_1_3_1_165_2","doi-asserted-by":"publisher","DOI":"10.1518\/155534308X284417"},{"key":"e_1_3_1_166_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSTW55395.2022.00030"},{"key":"e_1_3_1_167_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-021-01736-5"},{"key":"e_1_3_1_168_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cjca.2021.09.004"},{"key":"e_1_3_1_169_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744358"},{"key":"e_1_3_1_170_2","doi-asserted-by":"publisher","DOI":"10.24251\/HICSS.2022.186"},{"key":"e_1_3_1_171_2","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517886"},{"key":"e_1_3_1_172_2","unstructured":"Alun Preece Dan Harborne Dave Braines Richard Tomsett and Supriyo Chakraborty. 2018. Stakeholders in explainable AI. arXiv:1810.00184. Retrieved from http:\/\/arxiv.org\/abs\/1810.00184"},{"key":"e_1_3_1_173_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2007.04.004"},{"key":"e_1_3_1_174_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-09342-5_9"},{"key":"e_1_3_1_175_2","first-page":"502","article-title":"Towards a knowledge graph-based explainable decision support system in Healthcare","volume":"281","author":"Rajabi Enayat","year":"2021","unstructured":"Enayat Rajabi and Kobra Etminani. 2021. Towards a knowledge graph-based explainable decision support system in Healthcare. Studies in Health Technology and Informatics 281 (2021), 502\u2013503.","journal-title":"Studies in Health Technology and Informatics"},{"key":"e_1_3_1_176_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.13200"},{"key":"e_1_3_1_177_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_1_178_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"e_1_3_1_179_2","first-page":"8116","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Rieger Laura","year":"2020","unstructured":"Laura Rieger, Chandan Singh, William Murdoch, and Bin Yu. 2020. Interpretations are useful: Penalizing explanations to align neural networks with prior knowledge. In Proceedings of the 37th International Conference on Machine Learning. PMLR, 8116\u20138126."},{"key":"e_1_3_1_180_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-019-09408-y"},{"key":"e_1_3_1_181_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103523"},{"key":"e_1_3_1_182_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110273"},{"key":"e_1_3_1_183_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_1"},{"key":"e_1_3_1_184_2","unstructured":"Wojciech Samek Thomas Wiegand and Klaus-Robert M\u00fcller. 2017. Explainable artificial intelligence: Understanding visualizing and interpreting deep learning models. arXiv:1708.08296. Retrieved from http:\/\/arxiv.org\/abs\/1708.08296"},{"key":"e_1_3_1_185_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10121406"},{"key":"e_1_3_1_186_2","first-page":"149","volume-title":"Proceedings of the 6th IFAC\/IFIP\/IFORS\/IEA Symposium on Analysis, Design, and Evaluation of Man-Machine Systems","author":"Sarter N. B.","year":"1995","unstructured":"N. B. Sarter and D. D. Woods. 1995. Autonomy, authority, and observability: The evolution of critical automation properties and their impact on man-machine coordination and cooperation. In Proceedings of the 6th IFAC\/IFIP\/IFORS\/IEA Symposium on Analysis, Design, and Evaluation of Man-Machine Systems, 149\u2013152."},{"key":"e_1_3_1_187_2","volume-title":"Proceedings of Workshop on Visualization for the Digital Humanities (Vis4DH \u201916)","author":"Schmidt Benajmin","year":"2016","unstructured":"Benajmin Schmidt. 2016. A public exploratory data analysis of gender bias in teaching evaluations. In Proceedings of Workshop on Visualization for the Digital Humanities (Vis4DH \u201916), 1\u20134."},{"key":"e_1_3_1_188_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2021.102684"},{"key":"e_1_3_1_189_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0212-3"},{"key":"e_1_3_1_190_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_1_191_2","first-page":"480","volume-title":"Proceedings of the 14th International Joint Conference on Artificial Intelligence","volume":"1","author":"Setiono Rudy","year":"1995","unstructured":"Rudy Setiono and Huan Liu. 1995. Understanding neural networks via rule extraction. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Vol. 1, 480\u2013485."},{"key":"e_1_3_1_192_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3088832"},{"key":"e_1_3_1_193_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2017.0362"},{"key":"e_1_3_1_194_2","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3306006"},{"key":"e_1_3_1_195_2","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2022.2101698"},{"key":"e_1_3_1_196_2","first-page":"1","volume-title":"Proceedings of the Workshop at International Conference on Learning Representations","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Deep inside convolutional networks: Visualising image classification models and saliency maps. In Proceedings of the Workshop at International Conference on Learning Representations, 1\u20138. Citeseer."},{"key":"e_1_3_1_197_2","unstructured":"Daniel Smilkov Nikhil Thorat Been Kim Fernanda Vi\u00e9gas and Martin Wattenberg. 2017. SmoothGrad: Removing noise by adding noise. arXiv:1706.03825. Retrieved from http:\/\/arxiv.org\/abs\/1706.03825"},{"key":"e_1_3_1_198_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012489924661"},{"key":"e_1_3_1_199_2","unstructured":"Thilo Spinner Daniel F\u00fcrst and Mennatallah El-Assady. 2024. iNNspector: Visual interactive deep model debugging. arXiv:2407.17998. Retrieved from http:\/\/arxiv.org\/abs\/2407.17998"},{"key":"e_1_3_1_200_2","unstructured":"Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox and Martin Riedmiller. 2014. Striving for simplicity: The all convolutional net. arXiv:1412.6806. Retrieved from https:\/\/arxiv.org\/abs\/1412.6806"},{"key":"e_1_3_1_201_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00362"},{"key":"e_1_3_1_202_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_31"},{"key":"e_1_3_1_203_2","doi-asserted-by":"publisher","DOI":"10.1111\/bju.15122"},{"key":"e_1_3_1_204_2","doi-asserted-by":"crossref","unstructured":"Shimon Sumita Hiroyuki Nakagawa and Tatsuhiro Tsuchiya. 2023. Xtune: An XAI-based hyperparameter tuning method for time-series forecasting using deep learning. Retrieved from http:\/\/www.researchsquare.com\/article\/rs-3008932\/v1","DOI":"10.21203\/rs.3.rs-3008932\/v1"},{"key":"e_1_3_1_205_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376866"},{"key":"e_1_3_1_206_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445088"},{"key":"e_1_3_1_207_2","doi-asserted-by":"publisher","DOI":"10.1109\/CoG47356.2020.9231843"},{"key":"e_1_3_1_208_2","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314293"},{"key":"e_1_3_1_209_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME.2014.106"},{"key":"e_1_3_1_210_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098039"},{"key":"e_1_3_1_211_2","unstructured":"Richard Tomsett Dave Braines Dan Harborne Alun Preece and Supriyo Chakraborty. 2018. Interpretable to whom? A role-based model for analyzing interpretable machine learning systems. arXiv:1806.07552. Retrieved from http:\/\/arxiv.org\/abs\/1806.07552"},{"key":"e_1_3_1_212_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022683529158"},{"key":"e_1_3_1_213_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378102"},{"key":"e_1_3_1_214_2","doi-asserted-by":"publisher","DOI":"10.1002\/9780470979174"},{"key":"e_1_3_1_215_2","unstructured":"European Union. 2018. General data protection regulation (GDPR). Retrieved from https:\/\/gdpr-info.eu\/"},{"key":"e_1_3_1_216_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2020.102493"},{"key":"e_1_3_1_217_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.122079"},{"key":"e_1_3_1_218_2","doi-asserted-by":"publisher","DOI":"10.1145\/3677119"},{"key":"e_1_3_1_219_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-93736-2_39"},{"key":"e_1_3_1_220_2","doi-asserted-by":"publisher","DOI":"10.1177\/0018720818788164"},{"key":"e_1_3_1_221_2","unstructured":"Marina M.-C. Vidovic Nico G\u00f6rnitz Klaus-Robert M\u00fcller and Marius Kloft. 2016. Feature importance measure for non-linear learning algorithms. arXiv:1611.07567. Retrieved from http:\/\/arxiv.org\/abs\/1611.07567"},{"key":"e_1_3_1_222_2","unstructured":"Giulia Vilone and Luca Longo. 2020. Explainable artificial intelligence: A systematic review. arXiv:2006.00093. Retrieved from http:\/\/arxiv.org\/abs\/2006.00093"},{"key":"e_1_3_1_223_2","unstructured":"Klaus Virtanen. 2022. Using XAI tools to detect harmful bias in ML models. Bachelor's Thesis. Ume\u00e5 University Department of Computing Science."},{"key":"e_1_3_1_224_2","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the GDPR","volume":"31","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology 31 (2017), 841.","journal-title":"Harvard Journal of Law & Technology"},{"key":"e_1_3_1_225_2","doi-asserted-by":"publisher","unstructured":"Syed Wali and Irfan Khan. 2021. Explainable AI and random forest based reliable intrusion detection system. DOI: 10.36227\/techrxiv.17169080.v1","DOI":"10.36227\/techrxiv.17169080.v1"},{"key":"e_1_3_1_226_2","doi-asserted-by":"publisher","DOI":"10.1145\/3359313"},{"key":"e_1_3_1_227_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2988359"},{"key":"e_1_3_1_228_2","first-page":"6505","volume-title":"Proceedings of the International Conference on Machine Learning.","author":"Wang Tong","year":"2019","unstructured":"Tong Wang. 2019. Gaining free or low-cost interpretability with interpretable partial substitute. In Proceedings of the International Conference on Machine Learning. PMLR, 6505\u20136514."},{"key":"e_1_3_1_229_2","doi-asserted-by":"publisher","DOI":"10.1145\/3617380"},{"key":"e_1_3_1_230_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN54540.2023.10191221"},{"key":"e_1_3_1_231_2","unstructured":"Ziming Wang Changwu Huang and Xin Yao. 2024. Procedural fairness in machine learning. arXiv:2404.01877. Retrieved from http:\/\/arxiv.org\/abs\/2404.01877"},{"key":"e_1_3_1_232_2","unstructured":"Geoffrey I Webb Loong Kuan Lee Fran\u00e7ois Petitjean and Bart Goethals. 2017. Understanding concept drift. arXiv:1704.00362. http:\/\/arxiv.org\/abs\/1704.00362"},{"key":"e_1_3_1_233_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308532.3329441"},{"key":"e_1_3_1_234_2","unstructured":"Adrian Weller. 2017. Challenges for transparency. arXiv:1708.01870. Retrieved from http:\/\/arxiv.org\/abs\/1708.01870"},{"key":"e_1_3_1_235_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.alw-1.7"},{"key":"e_1_3_1_236_2","doi-asserted-by":"publisher","DOI":"10.5465\/amr.1999.1580443"},{"key":"e_1_3_1_237_2","doi-asserted-by":"publisher","DOI":"10.1145\/3290607.3312817"},{"key":"e_1_3_1_238_2","first-page":"29","volume-title":"Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining","volume":"1","author":"Wirth R\u00fcdiger","year":"2000","unstructured":"R\u00fcdiger Wirth and Jochen Hipp. 2000. CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, Vol. 1, Manchester, 29\u201339."},{"key":"e_1_3_1_239_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744878"},{"key":"e_1_3_1_240_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376807"},{"key":"e_1_3_1_241_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-44198-1_7"},{"key":"e_1_3_1_242_2","first-page":"521","article-title":"Explainable AI for COVID-19 CT classifiers: An initial comparison study","author":"Ye Qinghao","year":"2021","unstructured":"Qinghao Ye, Jun Xia, and Guang Yang. 2021. Explainable AI for COVID-19 CT classifiers: An initial comparison study. In Proceedings of the 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS \u201921). IEEE, 521\u2013526.","journal-title":"Proceedings of the 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS \u201921)."},{"key":"e_1_3_1_243_2","unstructured":"Jason Yosinski Jeff Clune Anh Nguyen Thomas Fuchs and Hod Lipson. 2015. Understanding neural networks through deep visualization. arXiv:1506.06579. Retrieved from http:\/\/arxiv.org\/abs\/1506.06579"},{"key":"e_1_3_1_244_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8512266"},{"key":"e_1_3_1_245_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"e_1_3_1_246_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00688"},{"key":"e_1_3_1_247_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.accinf.2022.100572"},{"key":"e_1_3_1_248_2","first-page":"2037","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence","author":"Zhang Junzhe","year":"2018","unstructured":"Junzhe Zhang and Elias Bareinboim. 2018. Fairness in decision-making\u2014The causal explanation formula. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2037\u20132045."},{"key":"e_1_3_1_249_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1059-x"},{"key":"e_1_3_1_250_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2021.3096824"},{"key":"e_1_3_1_251_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.414"},{"key":"e_1_3_1_252_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11819"},{"key":"e_1_3_1_253_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10924"},{"key":"e_1_3_1_254_2","first-page":"11","article-title":"Mining interpretable AOG representations from convolutional networks via active question answering","volume":"43","author":"Zhang Quanshi","year":"2020","unstructured":"Quanshi Zhang, Jie Ren, Ge Huang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu. 2020. Mining interpretable AOG representations from convolutional networks via active question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 11 (2020), 3949\u20133963.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_255_2","first-page":"11","article-title":"Extraction of an explanatory graph to interpret a CNN","volume":"43","author":"Zhang Quanshi","year":"2020","unstructured":"Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, and Song-Chun Zhu. 2020. Extraction of an explanatory graph to interpret a CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 11 (2020), 3863\u20133877.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_256_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00920"},{"key":"e_1_3_1_257_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00642"},{"key":"e_1_3_1_258_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2021.3100641"}],"container-title":["ACM Transactions on Autonomous and Adaptive Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3702004","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3702004","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:10:23Z","timestamp":1750295423000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3702004"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,24]]},"references-count":257,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12,31]]}},"alternative-id":["10.1145\/3702004"],"URL":"https:\/\/doi.org\/10.1145\/3702004","relation":{},"ISSN":["1556-4665","1556-4703"],"issn-type":[{"value":"1556-4665","type":"print"},{"value":"1556-4703","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,24]]},"assertion":[{"value":"2024-09-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}