{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T19:25:27Z","timestamp":1792092327169,"version":"4.4.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100016379","name":"Universit\u00e4t Osnabr\u00fcck","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100016379","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The growing number of applications of machine learning and data mining in many domains\u2014from agriculture to business, education, industrial manufacturing, and medicine\u2014gave rise to new requirements for how to inspect and control the learned models. The research domain of explainable artificial intelligence (XAI) has been newly established with a strong focus on methods being applied post-hoc on black-box models. As an alternative, the use of interpretable machine learning methods has been considered\u2014where the learned models are white-box ones. Black-box models can be characterized as representing implicit knowledge\u2014typically resulting from statistical and neural approaches of machine learning, while white-box models are explicit representations of knowledge\u2014typically resulting from rule-learning approaches. In this introduction to the special issue on \u2018Explainable and Interpretable Machine Learning and Data Mining\u2019 we propose to bring together both perspectives, pointing out commonalities and discussing possibilities to integrate them.<\/jats:p>","DOI":"10.1007\/s10618-024-01041-y","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T14:03:44Z","timestamp":1722348224000},"page":"2571-2595","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Explainable and interpretable machine learning and data mining"],"prefix":"10.1007","volume":"38","author":[{"given":"Martin","family":"Atzmueller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"F\u00fcrnkranz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom\u00e1\u0161","family":"Kliegr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ute","family":"Schmid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,30]]},"reference":[{"issue":"4","key":"1041_CR12","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1007\/s10618-022-00840-5","volume":"36","author":"Q Au","year":"2022","unstructured":"Au Q, Herbinger J, Stachl C et al (2022) Grouped feature importance and combined features effect plot. Data Min Knowl Disc 36(4):1401\u20131450. https:\/\/doi.org\/10.1007\/s10618-022-00840-5","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-023-00924-w","author":"H Baniecki","year":"2023","unstructured":"Baniecki H, Parzych D, Biecek P (2023) The grammar of interactive explanatory model analysis. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-023-00924-w","journal-title":"Data Min Knowl Disc"},{"issue":"5","key":"1041_CR155","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1007\/s10618-023-00933-9","volume":"37","author":"F Bodria","year":"2023","unstructured":"Bodria F, Giannotti F, Guidotti R et al (2023) Benchmarking and survey of explanation methods for black box models. Data Min Knowl Disc 37(5):1719\u20131778. https:\/\/doi.org\/10.1007\/s10618-023-00933-9","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00896-3","author":"A Brands\u00e6ter","year":"2023","unstructured":"Brands\u00e6ter A, Glad IK (2023) Shapley values for cluster importance. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00896-3","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-023-00930-y","author":"D Brughmans","year":"2023","unstructured":"Brughmans D, Leyman P, Martens D (2023) NICE: an algorithm for nearest instance counterfactual explanations. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-023-00930-y","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-023-00927-7","author":"B Coma-Puig","year":"2023","unstructured":"Coma-Puig B, Calvo A, Carmona J et al (2023) A case study of improving a non-technical losses detection system through explainability. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-023-00927-7","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00889-2","author":"R Crupi","year":"2023","unstructured":"Crupi R, Castelnovo A, Regoli D et al (2023) Counterfactual explanations as interventions in latent space. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00889-2","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00831-6","author":"R Guidotti","year":"2023","unstructured":"Guidotti R (2023) Counterfactual explanations and how to find them: literature review and benchmarking. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00831-6","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR54","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00878-5","author":"R Guidotti","year":"2023","unstructured":"Guidotti R, Monreale A, Ruggieri S et al (2023) Stable and actionable explanations of black-box models through factual and counterfactual rules. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00878-5","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00892-7","author":"SS Hada","year":"2023","unstructured":"Hada SS, Carreira-Perpi\u00f1\u00e1n M\u00c1, Zharmagambetov A (2023) Sparse oblique decision trees: a tool to understand and manipulate neural net features. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00892-7","journal-title":"Data Min Knowl Disc"},{"issue":"4","key":"1041_CR72","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1007\/s10618-022-00830-7","volume":"36","author":"N Javed","year":"2022","unstructured":"Javed N, Gobet F, Lane P (2022) Simplification of genetic programs: a literature survey. Data Min Knowl Disc 36(4):1279\u20131300. https:\/\/doi.org\/10.1007\/s10618-022-00830-7","journal-title":"Data Min Knowl Disc"},{"issue":"4","key":"1041_CR90","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1007\/s10618-022-00841-4","volume":"36","author":"M Merz","year":"2022","unstructured":"Merz M, Richman R, Tsanakas A et al (2022) Interpreting deep learning models with marginal attribution by conditioning on quantiles. Data Min Knowl Disc 36(4):1335\u20131370. https:\/\/doi.org\/10.1007\/s10618-022-00841-4","journal-title":"Data Min Knowl Disc"},{"issue":"4","key":"1041_CR94","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1007\/s10618-022-00839-y","volume":"36","author":"I Mollas","year":"2022","unstructured":"Mollas I, Bassiliades N, Tsoumakas G (2022) Conclusive local interpretation rules for random forests. Data Min Knowl Disc 36(4):1521\u20131574. https:\/\/doi.org\/10.1007\/s10618-022-00839-y","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR96","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00901-9","author":"C Molnar","year":"2023","unstructured":"Molnar C, K\u00f6nig G, Bischl B et al (2023) Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00901-9","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR110","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00906-4","author":"MM Raimundo","year":"2023","unstructured":"Raimundo MM, Nonato LG, Poco J (2023) Mining Pareto-optimal counterfactual antecedents with a branch-and-bound model-agnostic algorithm. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00906-4","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR123","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-023-00920-0","author":"J Schneider","year":"2023","unstructured":"Schneider J, Vlachos M (2023) Reflective-net: learning from explanations. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-023-00920-0","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR126","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-023-00993-x","author":"CA Scholbeck","year":"2024","unstructured":"Scholbeck CA, Casalicchio G, Molnar C et al (2024) Marginal effects for non-linear prediction functions. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-023-00993-x","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR127","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00867-8","author":"G Schwalbe","year":"2023","unstructured":"Schwalbe G, Finzel B (2023) A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00867-8","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR133","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-024-01010-5","author":"K Sokol","year":"2024","unstructured":"Sokol K, Flach P (2024) Interpretable representations in explainable AI: from theory to practice. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-024-01010-5","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR134","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00872-x","author":"F Sovrano","year":"2023","unstructured":"Sovrano F, Vitali F (2023) ExplanatorY Artificial Intelligence (YAI): human-centered explanations of explainable AI and complex data. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00872-x","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"1041_CR140","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1007\/s10618-022-00821-8","volume":"36","author":"F Valente","year":"2022","unstructured":"Valente F, Paredes S, Henriques J et al (2022) Interpretability, personalization and reliability of a machine learning based clinical decision support system. Data Min Knowl Disc 36(3):1140\u20131173. https:\/\/doi.org\/10.1007\/s10618-022-00821-8","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR141","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-023-00915-x","author":"F Ventura","year":"2023","unstructured":"Ventura F, Greco S, Apiletti D et al (2023) Explaining deep convolutional models by measuring the influence of interpretable features in image classification. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-023-00915-x","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR142","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00870-z","author":"L Veyrin-Forrer","year":"2023","unstructured":"Veyrin-Forrer L, Kamal A, Duffner S et al (2023) On GNN explainability with activation rules. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00870-z","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR145","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00900-w","author":"D Vre\u0161","year":"2023","unstructured":"Vre\u0161 D, Robnik-\u0160ikonja M (2023) Preventing deception with explanation methods using focused sampling. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00900-w","journal-title":"Data Min Knowl Disc"},{"key":"1041_CR154","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00907-3","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Zhou Z, Hooker G (2023) Approximation trees: statistical reproducibility in model distillation. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00907-3","journal-title":"Data Min Knowl Disc"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-024-01041-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-024-01041-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-024-01041-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T08:14:11Z","timestamp":1725092051000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-024-01041-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,30]]},"references-count":25,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["1041"],"URL":"https:\/\/doi.org\/10.1007\/s10618-024-01041-y","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,30]]},"assertion":[{"value":"22 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}