{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T16:55:15Z","timestamp":1762102515354,"version":"3.40.4"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030461461"},{"type":"electronic","value":"9783030461478"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-46147-8_3","type":"book-chapter","created":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T02:03:39Z","timestamp":1588298619000},"page":"37-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Unjustified Classification Regions and Counterfactual Explanations in Machine Learning"],"prefix":"10.1007","author":[{"given":"Thibault","family":"Laugel","sequence":"first","affiliation":[]},{"given":"Marie-Jeanne","family":"Lesot","sequence":"additional","affiliation":[]},{"given":"Christophe","family":"Marsala","sequence":"additional","affiliation":[]},{"given":"Xavier","family":"Renard","sequence":"additional","affiliation":[]},{"given":"Marcin","family":"Detyniecki","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"3_CR1","unstructured":"Alvarez Melis, D., Jaakkola, T.: Towards robust interpretability with self-explaining neural networks. In: Advances in Neural Information Processing Systems, vol. 31, pp. 7786\u20137795 (2018)"},{"key":"3_CR2","first-page":"1803","volume":"11","author":"D Baehrens","year":"2010","unstructured":"Baehrens, D., Schroeter, T., Harmeling, S., Hansen, K., Muller, K.R.: How to explain individual classification decisions Motoaki Kawanabe. J. Mach. Learn. Res. 11, 1803\u20131831 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/978-3-642-40994-3_25","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"B Biggio","year":"2013","unstructured":"Biggio, B., et al.: Evasion attacks against machine learning at test time. In: Blockeel, H., Kersting, K., Nijssen, S., \u017delezn\u00fd, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8190, pp. 387\u2013402. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40994-3_25"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.patcog.2018.07.023","volume":"84","author":"B Biggio","year":"2018","unstructured":"Biggio, B., Roli, F.: Wild patterns: ten years after the rise of adversarial machine learning. Pattern Recogn. 84, 317\u2013331 (2018)","journal-title":"Pattern Recogn."},{"key":"3_CR5","first-page":"3207","volume":"14","author":"L Bottou","year":"2013","unstructured":"Bottou, L., et al.: Counterfactual reasoning and learning systems: the example of computational advertising. J. Mach. Learn. Res. 14, 3207\u20133260 (2013)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR6","unstructured":"Craven, M.W., Shavlik, J.W.: Extracting tree-structured representations of trained neural networks. In: Advances in Neural Information Processing Systems, vol. 8, pp. 24\u201330 (1996)"},{"key":"3_CR7","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017)"},{"key":"3_CR8","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226\u2013231 (1996)"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Fawzi, A., Moosavi-Dezfooli, S.M., Frossard, P., Soatto, S.: Empirical study of the topology and geometry of deep networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00396"},{"key":"3_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/978-3-319-23485-4_53","volume-title":"Progress in Artificial Intelligence","author":"K Fernandes","year":"2015","unstructured":"Fernandes, K., Vinagre, P., Cortez, P.: A proactive intelligent decision support system for predicting the popularity of online news. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds.) EPIA 2015. LNCS (LNAI), vol. 9273, pp. 535\u2013546. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-23485-4_53"},{"key":"3_CR11","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., Giannotti, F.: Local rule-based explanations of black box decision systems. arXiv preprint arXiv:1805.10820 (2018)"},{"issue":"5","key":"3_CR12","first-page":"93","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 Comput. Surv. (CSUR) 51(5), 93 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"3_CR13","unstructured":"Hara, S., Hayashi, K.: Making tree ensembles interpretable. In: ICML Workshop on Human Interpretability in Machine Learning (WHI 2016) (2016)"},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/0095-0696(78)90006-2","volume":"5","author":"D Harrison","year":"1978","unstructured":"Harrison, D., Rubinfeld, D.: Hedonic prices and the demand for clean air. Environ. Econ. Manag. 5, 81\u2013102 (1978)","journal-title":"Environ. Econ. Manag."},{"key":"3_CR15","unstructured":"Jiang, H., Kim, B., Guan, M., Gupta, M.: To trust or not to trust a classifier. In: Advances in Neural Information Processing Systems, vol. 31, pp. 5541\u20135552 (2018)"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Kabra, M., Robie, A., Branson, K.: Understanding classifier errors by examining influential neighbors. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3917\u20133925 (2015)","DOI":"10.1109\/CVPR.2015.7299017"},{"key":"3_CR17","unstructured":"Kim, B., Rudin, C., Shah, J.A.: The Bayesian case model: a generative approach for case-based reasoning and prototype classification. In: Advances in Neural Information Processing Systems, pp. 1952\u20131960 (2014)"},{"key":"3_CR18","volume-title":"How We Analyzed the COMPAS Recidivism Algorithm","author":"J Larson","year":"2016","unstructured":"Larson, J., Mattu, S., Kirchner, L., Angwin, J.: How We Analyzed the COMPAS Recidivism Algorithm. ProPublica, Manhattan (2016)"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Lash, M., Lin, Q., Street, N., Robinson, J., Ohlmann, J.: Generalized inverse classification. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 162\u2013170 (2017)","DOI":"10.1137\/1.9781611974973.19"},{"key":"3_CR20","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-319-91473-2_9","volume-title":"Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations","author":"T Laugel","year":"2018","unstructured":"Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., Detyniecki, M.: Comparison-based inverse classification for interpretability in machine learning. In: Medina, J., et al. (eds.) IPMU 2018. CCIS, vol. 853, pp. 100\u2013111. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-91473-2_9"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Laugel, T., Lesot, M.J., Marsala, C., Renard, X., Detyniecki, M.: The dangers of post-hoc interpretability: Unjustified counterfactual explanations. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence IJCAI 2019 (2019, to appear)","DOI":"10.24963\/ijcai.2019\/388"},{"key":"3_CR22","unstructured":"Laugel, T., Renard, X., Lesot, M.J., Marsala, C., Detyniecki, M.: Defining locality for surrogates in post-hoc interpretablity. In: ICML Workshop on Human Interpretability in Machine Learning (WHI 2018) (2018)"},{"key":"3_CR23","unstructured":"Lipton, Z.C.: The mythos of model interpretability. In: ICML Workshop on Human Interpretability in Machine Learning (WHI 2017) (2017)"},{"key":"3_CR24","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30, pp. 4765\u20134774 (2017)"},{"issue":"1","key":"3_CR25","doi-asserted-by":"publisher","first-page":"73","DOI":"10.25300\/MISQ\/2014\/38.1.04","volume":"38","author":"D Martens","year":"2014","unstructured":"Martens, D., Provost, F.: Explaining data-driven document classifications. MIS Q. 38(1), 73\u2013100 (2014)","journal-title":"MIS Q."},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \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, KDD 2016, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"3_CR27","unstructured":"Rudin, C.: Please stop explaining black box models for high stakes decisions. In: NeurIPS Workshop on Critiquing and Correcting Trends in Machine Learning (2018)"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Russell, C.: Efficient search for diverse coherent explanations. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, (FAT* 2019), pp. 20\u201328 (2019)","DOI":"10.1145\/3287560.3287569"},{"key":"3_CR29","unstructured":"Turner, R.: A model explanation system. In: NIPS Workshop on Black Box Learning and Inference (2015)"},{"issue":"2","key":"3_CR30","first-page":"841","volume":"31","author":"S Wachter","year":"2018","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box; automated decisions and the GDPR. Harv. J. Law Technol. 31(2), 841\u2013887 (2018)","journal-title":"Harv. J. Law Technol."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46147-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T09:32:02Z","timestamp":1746523922000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46147-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461461","9783030461478"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46147-8_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","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":"130","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":"18% - 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":"3.04","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":"5.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}