{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T22:14:28Z","timestamp":1784585668333,"version":"3.55.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031188398","type":"print"},{"value":"9783031188404","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\/978-3-031-18840-4_35","type":"book-chapter","created":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T07:04:51Z","timestamp":1667631891000},"page":"493-508","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["JUICE: JUstIfied Counterfactual Explanations"],"prefix":"10.1007","author":[{"given":"Alejandro","family":"Kuratomi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioanna","family":"Miliou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zed","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tony","family":"Lindgren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Papapetrou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Bobek, S., Nalepa, G.J.: Explainability in knowledge discovery from data streams. In: 2019 First International Conference on Societal Automation (SA), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/SA47457.2019.8938075"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Boer, N., Deutch, D., Frost, N., Milo, T.: Just in time: personal temporal insights for altering model decisions. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1988\u20131991. IEEE (2019)","DOI":"10.1109\/ICDE.2019.00221"},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"Byrne, R.M.: Counterfactuals in explainable artificial intelligence (XAI): evidence from human reasoning. In: IJCAI, pp. 6276\u20136282 (2019)","DOI":"10.24963\/ijcai.2019\/876"},{"key":"35_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1007\/978-3-030-58112-1_31","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XVI","author":"S Dandl","year":"2020","unstructured":"Dandl, S., Molnar, C., Binder, M., Bischl, B.: Multi-objective counterfactual explanations. In: B\u00e4ck, T. (ed.) PPSN 2020. LNCS, vol. 12269, pp. 448\u2013469. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58112-1_31"},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Dodge, J., Liao, Q.V., Zhang, Y., Bellamy, R.K., Dugan, C.: Explaining models: an empirical study of how explanations impact fairness judgment. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 275\u2013285 (2019)","DOI":"10.1145\/3301275.3302310"},{"key":"35_CR6","unstructured":"Karimi, A.H., Barthe, G., Balle, B., Valera, I.: Model-agnostic counterfactual explanations for consequential decisions. In: International Conference on Artificial Intelligence and Statistics, pp. 895\u2013905. PMLR (2020)"},{"key":"35_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103495","volume":"108","author":"E Kyrimi","year":"2020","unstructured":"Kyrimi, E., Neves, M.R., McLachlan, S., Neil, M., Marsh, W., Fenton, N.: Medical idioms for clinical Bayesian network development. J. Biomed. Inform. 108, 103495 (2020)","journal-title":"J. Biomed. Inform."},{"key":"35_CR8","unstructured":"Laugel, T., Lesot, M.J., Marsala, C., Detyniecki, M.: Issues with post-hoc counterfactual explanations: a discussion. arXiv preprint arXiv:1906.04774 (2019)"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Laugel, T., Lesot, M.J., Marsala, C., Renard, X., Detyniecki, M.: Inverse classification for comparison-based interpretability in machine learning. arXiv preprint arXiv:1712.08443 (2017)","DOI":"10.1007\/978-3-319-91473-2_9"},{"key":"35_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-3-030-46147-8_3","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"T Laugel","year":"2020","unstructured":"Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., Detyniecki, M.: Unjustified classification regions and counterfactual explanations in machine learning. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11907, pp. 37\u201354. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46147-8_3"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Lindgren, T., Papapetrou, P., Samsten, I., Asker, L.: Example-based feature tweaking using random forests. In: 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), pp. 53\u201360. IEEE (2019)","DOI":"10.1109\/IRI.2019.00022"},{"key":"35_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019)","journal-title":"Artif. Intell."},{"key":"35_CR13","unstructured":"Molnar, C.: Interpretable machine learning: a guide for making black-box models explainable (2021). https:\/\/christophm.github.io\/interpretable-ml-book\/limo.html"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607\u2013617 (2020)","DOI":"10.1145\/3351095.3372850"},{"key":"35_CR15","unstructured":"Pawelczyk, M., Bielawski, S., Heuvel, J.v.d., Richter, T., Kasneci, G.: CARLA: a python library to benchmark algorithmic recourse and counterfactual explanation algorithms. arXiv preprint arXiv:2108.00783 (2021)"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Pawelczyk, M., Broelemann, K., Kasneci, G.: Learning model-agnostic counterfactual explanations for tabular data. In: Proceedings of The Web Conference 2020, pp. 3126\u20133132 (2020)","DOI":"10.1145\/3366423.3380087"},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., Flach, P.: Face: feasible and actionable counterfactual explanations. In: Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society, pp. 344\u2013350 (2020)","DOI":"10.1145\/3375627.3375850"},{"issue":"5","key":"35_CR18","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206\u2013215 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Tolomei, G., Silvestri, F., Haines, A., Lalmas, M.: Interpretable predictions of tree-based ensembles via actionable feature tweaking. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 465\u2013474 (2017)","DOI":"10.1145\/3097983.3098039"},{"key":"35_CR20","unstructured":"Verma, S., Dickerson, J., Hines, K.: Counterfactual explanations for machine learning: a review. arXiv:2010.10596 (2020)"},{"key":"35_CR21","first-page":"841","volume":"31","author":"S Wachter","year":"2017","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017)","journal-title":"Harv. JL Tech."},{"issue":"1","key":"35_CR22","first-page":"56","volume":"26","author":"J Wexler","year":"2019","unstructured":"Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Vi\u00e9gas, F., Wilson, J.: The what-if tool: interactive probing of machine learning models. IEEE Trans. Vis. Comput. Graph. 26(1), 56\u201365 (2019)","journal-title":"IEEE Trans. Vis. Comput. Graph."}],"container-title":["Lecture Notes in Computer Science","Discovery Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18840-4_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T07:11:25Z","timestamp":1667632285000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18840-4_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031188398","9783031188404"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18840-4_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Discovery Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montpellier","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"10 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ds2022.sciencesconf.org\/","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":"59","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":"27","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":"12","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":"46% - 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":"2.5","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":"2.5","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)"}}]}}