{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:23:34Z","timestamp":1778801014159,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031074806","type":"print"},{"value":"9783031074813","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-07481-3_7","type":"book-chapter","created":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T06:02:51Z","timestamp":1653631371000},"page":"55-63","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Meta Survey of\u00a0Quality Evaluation Criteria in\u00a0Explanation Methods"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9633-0423","authenticated-orcid":false,"given":"Helena","family":"L\u00f6fstr\u00f6m","sequence":"first","affiliation":[]},{"given":"Karl","family":"Hammar","sequence":"additional","affiliation":[]},{"given":"Ulf","family":"Johansson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,28]]},"reference":[{"issue":"44","key":"7_CR1","doi-asserted-by":"publisher","first-page":"22071","DOI":"10.1073\/pnas.1900654116","volume":"116","author":"WJ Murdoch","year":"2019","unstructured":"Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B.: Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. 116(44), 22071\u201322080 (2019)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.jbusres.2019.07.039","volume":"104","author":"H Snyder","year":"2019","unstructured":"Snyder, H.: Literature review as a research methodology: an overview and guidelines. J. Bus. Res. 104, 333\u2013339 (2019)","journal-title":"J. Bus. Res."},{"key":"7_CR3","unstructured":"Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. xiii\u2013xxiii (2002)"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"L\u00f6fstr\u00f6m, H., Hammar, K., Johansson, U.: A meta survey of quality evaluation criteria in explanation methods (2022)","DOI":"10.1007\/978-3-031-07481-3_7"},{"key":"7_CR5","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4768\u20134777 (2017)"},{"key":"7_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113941","volume":"165","author":"M Moradi","year":"2021","unstructured":"Moradi, M., Samwald, M.: Post-hoc explanation of black-box classifiers using confident itemsets. Expert Syst. Appl. 165, 113941 (2021)","journal-title":"Expert Syst. Appl."},{"key":"7_CR7","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":"7_CR8","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80\u201389. IEEE (2018)","DOI":"10.1109\/DSAA.2018.00018"},{"key":"7_CR10","unstructured":"Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Metrics for explainable AI: challenges and prospects. arXiv preprint arXiv:1812.04608 (2018)"},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"832","DOI":"10.3390\/electronics8080832","volume":"8","author":"DV Carvalho","year":"2019","unstructured":"Carvalho, D.V., Pereira, E.M., Cardoso, J.S.: Machine learning interpretability: a survey on methods and metrics. Electronics 8, 832 (2019)","journal-title":"Electronics"},{"key":"7_CR12","unstructured":"Mueller, S.T., Hoffman, R.R., Clancey, W., Emrey, A., Klein, G.: Explanation in human-AI systems: a literature meta-review, synopsis of key ideas and publications, and bibliography for explainable AI. arXiv preprint arXiv:1902.01876 (2019)"},{"key":"7_CR13","unstructured":"Mohseni, S., Zarei, N., Ragan, E.D.: A multidisciplinary survey and framework for design and evaluation of explainable AI systems. arXiv, pp. arXiv-1811 (2018)"},{"issue":"2","key":"7_CR14","first-page":"44","volume":"40","author":"D Gunning","year":"2019","unstructured":"Gunning, D., Aha, D.W.: Darpa\u2019s explainable artificial intelligence program. AI Mag 40(2), 44\u201358 (2019)","journal-title":"AI Mag"},{"issue":"3","key":"7_CR15","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1177\/0018720814547570","volume":"57","author":"KA Hoff","year":"2015","unstructured":"Hoff, K.A., Bashir, M.: Trust in automation: integrating empirical evidence on factors that influence trust. Hum. Factors 57(3), 407\u2013434 (2015)","journal-title":"Hum. Factors"},{"issue":"5","key":"7_CR16","doi-asserted-by":"publisher","first-page":"593","DOI":"10.3390\/electronics10050593","volume":"10","author":"J Zhou","year":"2021","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), 593 (2021)","journal-title":"Electronics"},{"issue":"6","key":"7_CR17","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/S1071-5819(03)00038-7","volume":"58","author":"MT Dzindolet","year":"2003","unstructured":"Dzindolet, M.T., Peterson, S.A., Pomranky, R.A., Pierce, L.G., Beck, H.P.: The role of trust in automation reliance. Int. J. Hum Comput Stud. 58(6), 697\u2013718 (2003)","journal-title":"Int. J. Hum Comput Stud."},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Pavlidis, M., Mouratidis, H., Islam, S., Kearney, P.: Dealing with trust and control: a meta-model for trustworthy information systems development. In: 2012 Sixth International Conference on Research Challenges in Information Science (RCIS), pp. 1\u20139. IEEE (2012)","DOI":"10.1109\/RCIS.2012.6240441"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Yang, F., Huang, Z., Scholtz, J., Arendt, D.L.: How do visual explanations foster end users\u2019 appropriate trust in machine learning? In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 189\u2013201 (2020)","DOI":"10.1145\/3377325.3377480"},{"key":"7_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/11429760_2","volume-title":"Trust Management","author":"S Marsh","year":"2005","unstructured":"Marsh, S., Dibben, M.R.: Trust, untrust, distrust and mistrust \u2013 an exploration of the dark(er) side. In: Herrmann, P., Issarny, V., Shiu, S. (eds.) iTrust 2005. LNCS, vol. 3477, pp. 17\u201333. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11429760_2"},{"issue":"1","key":"7_CR21","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1109\/THMS.2017.2776209","volume":"48","author":"F Ekman","year":"2017","unstructured":"Ekman, F., Johansson, M., Sochor, J.: Creating appropriate trust in automated vehicle systems: a framework for HMI design. IEEE Trans. Hum. Mach. Syst. 48(1), 95\u2013101 (2017)","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"McDermott, P.L., Ten Brink, R.N.: Practical guidance for evaluating calibrated trust. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 63, pp. 362\u2013366. SAGE Publications Sage CA, Los Angeles (2019)","DOI":"10.1177\/1071181319631379"},{"key":"7_CR23","unstructured":"Chromik, M., Schuessler, M.: A taxonomy for human subject evaluation of black-box explanations in xai. In ExSS-ATEC@ IUI (2020)"},{"key":"7_CR24","unstructured":"Das, A., Rad, P.: Opportunities and challenges in explainable artificial intelligence (XAI): a survey. arXiv preprint arXiv:2006.11371 (2020)"},{"key":"7_CR25","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138\u201352160 (2018)","journal-title":"IEEE Access"},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Wang, D., Yang, Q., Abdul, A., Lim, B.Y.: Designing theory-driven user-centric explainable AI. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, pp. 1\u201315, New York, Association for Computing Machinery (2019)","DOI":"10.1145\/3290605.3300831"},{"key":"7_CR27","unstructured":"Zhang, Y., Chen, X.: Explainable recommendation: a survey and new perspectives. arXiv preprint arXiv:1804.11192 (2018)"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Holzinger, A., Carrington, A., M\u00fcller, H.: Measuring the quality of explanations: the system causability scale (SCS). KI-K\u00fcnstliche Intelligenz, pp. 1\u20136 (2020)","DOI":"10.1007\/s13218-020-00636-z"},{"issue":"1","key":"7_CR29","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/e23010018","volume":"23","author":"P Linardatos","year":"2021","unstructured":"Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable AI: a review of machine learning interpretability methods. Entropy 23(1), 18 (2021)","journal-title":"Entropy"}],"container-title":["Lecture Notes in Business Information Processing","Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-07481-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,29]],"date-time":"2022-05-29T23:10:32Z","timestamp":1653865832000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-07481-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031074806","9783031074813"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-07481-3_7","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAiSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leuven","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caise2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/caise22.ugent.be\/","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":"203","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":"31","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":"15% - 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":"2-3","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":"6-10","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)"}}]}}