{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T08:02:08Z","timestamp":1776672128023,"version":"3.51.2"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002957","name":"Technische Universit\u00e4t Dresden","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002957","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Bus Inf Syst Eng"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s12599-025-00964-0","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T15:39:27Z","timestamp":1757950767000},"page":"445-463","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Inherently Interpretable Machine Learning: A Contrasting Paradigm to Post-hoc Explainable AI"],"prefix":"10.1007","volume":"68","author":[{"given":"Patrick","family":"Zschech","sequence":"first","affiliation":[]},{"given":"Sven","family":"Weinzierl","sequence":"additional","affiliation":[]},{"given":"Mathias","family":"Kraus","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"issue":"3","key":"964_CR1","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/s12525-021-00459-2","volume":"31","author":"BM Abdel-Karim","year":"2021","unstructured":"Abdel-Karim BM, Pfeuffer N, Hinz O (2021) Machine learning in information systems: a bibliographic review and open research issues. Electron Mark 31(3):643\u2013670","journal-title":"Electron Mark"},{"key":"964_CR2","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 (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6:52138\u201352160","journal-title":"IEEE Access"},{"key":"964_CR3","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1146\/annurev-statistics-040120-030919","volume":"11","author":"GI Allen","year":"2023","unstructured":"Allen GI, Gan L, Zheng L (2023) Interpretable machine learning for discovery: statistical challenges and opportunities. Annu Rev Stat Appl 11:97\u2013121","journal-title":"Annu Rev Stat Appl"},{"issue":"4","key":"964_CR4","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1111\/rssb.12377","volume":"82","author":"DW Apley","year":"2020","unstructured":"Apley DW, Zhu J (2020) Visualizing the effects of predictor variables in black box supervised learning models. J R Stat Soc Ser B Stat Methodol 82(4):1059\u20131086","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"964_CR5","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A Barredo Arrieta","year":"2020","unstructured":"Barredo Arrieta A, D\u00edaz-Rodr\u00edguez N, Ser J, Bennetot A, Tabik S, Barbado A, Garcia S, Gil-Lopez S, Molina D, Benjamins R, Chatila R, Herrera F (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58:82\u2013115","journal-title":"Inf Fusion"},{"key":"964_CR6","unstructured":"Basel Committee (2024) Basel III: international regulatory framework for banks. https:\/\/www.bis.org\/bcbs\/basel3.htm. Accessed 23 November 2024"},{"issue":"2","key":"964_CR7","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s12599-021-00683-2","volume":"63","author":"K Bauer","year":"2021","unstructured":"Bauer K, Hinz O, Aalst W, Weinhardt C (2021) Expl(AI)n it to me \u2013 explainable AI and information systems research. Bus Inf Syst Eng 63(2):79\u201382","journal-title":"Bus Inf Syst Eng"},{"key":"964_CR8","doi-asserted-by":"crossref","unstructured":"Beckh K, M\u00fcller S, Jakobs M, Toborek V, Tan H, Fischer R, Welke P, Houben S, von Rueden L (2023) Harnessing prior knowledge for explainable machine learning: an overview. In: 2023 IEEE conference on secure and trustworthy machine learning, pp 450\u2013463","DOI":"10.1109\/SaTML54575.2023.00038"},{"issue":"3","key":"964_CR9","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.25300\/MISQ\/2021\/16274","volume":"45","author":"N Berente","year":"2021","unstructured":"Berente N, Gu B, Recker J, Santhanam R (2021) Special issue editor\u2019s comments: managing artificial intelligence. Manag Inf Syst Q 45(3):1433\u20131450","journal-title":"Manag Inf Syst Q"},{"key":"964_CR10","unstructured":"Bereska L, Gavves E (2024) Mechanistic interpretability for AI safety \u2013 a review. arXiv:2404.14082"},{"key":"964_CR11","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1007\/s10994-017-5633-9","volume":"106","author":"D Bertsimas","year":"2017","unstructured":"Bertsimas D, Dunn J (2017) Optimal classification trees. Mach Learn 106:1039\u20131082","journal-title":"Mach Learn"},{"issue":"2","key":"964_CR12","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1007\/s10479-024-06226-8","volume":"347","author":"L Bohlen","year":"2025","unstructured":"Bohlen L, Rosenberger J, Zschech P, Kraus M (2025) Leveraging interpretable machine learning in intensive care. Ann Oper Res 347(2):1093\u20131132","journal-title":"Ann Oper Res"},{"issue":"1","key":"964_CR13","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s12525-023-00644-5","volume":"33","author":"J Brasse","year":"2023","unstructured":"Brasse J, Broder HR, F\u00f6rster M, Klier M, Sigler I (2023) Explainable artificial intelligence in information systems: a review of the status quo and future research directions. Electron Mark 33(1):26","journal-title":"Electron Mark"},{"key":"964_CR14","doi-asserted-by":"crossref","unstructured":"Broniatowski DA (2021) Psychological foundations of explainability and interpretability in artificial intelligence. In: Internal report 8367. National Institute of Standards and Technology","DOI":"10.6028\/NIST.IR.8367"},{"key":"964_CR15","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1613\/jair.1.12228","volume":"70","author":"N Burkart","year":"2021","unstructured":"Burkart N, Huber MF (2021) A survey on the explainability of supervised machine learning. J Artif Intell Res 70:245\u2013317","journal-title":"J Artif Intell Res"},{"key":"964_CR16","doi-asserted-by":"crossref","unstructured":"Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N (2015) Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1721\u20131730","DOI":"10.1145\/2783258.2788613"},{"key":"964_CR17","unstructured":"Chen C, Lin K, Rudin C, Shaposhnik Y, Wang S, Wang T (2018) An interpretable model with globally consistent explanations for credit risk. arXiv:1811.12615"},{"key":"964_CR18","doi-asserted-by":"crossref","unstructured":"Chromik M, Eiband M, Buchner F, Kr\u00fcger A, Butz A (2021) I think I get your point, AI! The illusion of explanatory depth in explainable AI. In: Proceedings of the 26th international conference on intelligent user interfaces, pp 307\u2013317","DOI":"10.1145\/3397481.3450644"},{"issue":"113","key":"964_CR19","first-page":"523","volume":"150","author":"KW Bock","year":"2021","unstructured":"Bock KW, Caigny A (2021) Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling. Decis Support Syst 150(113):523","journal-title":"Decis Support Syst"},{"issue":"2","key":"964_CR20","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.ejor.2018.02.009","volume":"269","author":"A De Caigny","year":"2018","unstructured":"De Caigny A, Coussement K, De Bock KW (2018) A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur J Oper Res 269(2):760\u2013772","journal-title":"Eur J Oper Res"},{"key":"964_CR21","doi-asserted-by":"crossref","unstructured":"Deck L, Schoeffer J, De-Arteaga M, K\u00fchl N (2024) A critical survey on fairness benefits of explainable AI. In: The 2024 ACM conference on fairness, accountability, and transparency, pp 1579\u20131595","DOI":"10.1145\/3630106.3658990"},{"key":"964_CR22","unstructured":"Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. arXiv:1702.08608"},{"issue":"1","key":"964_CR23","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1145\/3359786","volume":"63","author":"M Du","year":"2019","unstructured":"Du M, Liu N, Hu X (2019) Techniques for interpretable machine learning. Commun ACM 63(1):68\u201377","journal-title":"Commun ACM"},{"issue":"2","key":"964_CR24","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s12599-023-00842-7","volume":"66","author":"S Duda","year":"2024","unstructured":"Duda S, Hofmann P, Urbach N, V\u00f6lter F, Zwickel A (2024) The impact of resource allocation on the machine learning lifecycle: bridging the gap between software engineering and management. Bus Inf Syst Eng 66(2):203\u2013219","journal-title":"Bus Inf Syst Eng"},{"key":"964_CR25","unstructured":"European Parliament and Council (2024) Regulation (EU) 2024\/1689 of the European parliament and of the council. https:\/\/eur-lex.europa.eu\/eli\/reg\/2024\/1689\/oj. Accessed 23 November 2024"},{"issue":"4","key":"964_CR26","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s12599-020-00650-3","volume":"62","author":"S Feuerriegel","year":"2020","unstructured":"Feuerriegel S, Dolata M, Schwabe G (2020) Fair AI. Bus Inf Syst Eng 62(4):379\u2013384","journal-title":"Bus Inf Syst Eng"},{"issue":"4","key":"964_CR27","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1038\/s41591-024-02902-1","volume":"30","author":"S Feuerriegel","year":"2024","unstructured":"Feuerriegel S, Frauen D, Melnychuk V, Schweisthal J, Hess K, Curth A, Bauer S, Kilbertus N, Kohane IS, van der Schaar M (2024) Causal machine learning for predicting treatment outcomes. Nat Med 30(4):958\u2013968","journal-title":"Nat Med"},{"issue":"3","key":"964_CR28","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s11023-024-09691-z","volume":"34","author":"T Freiesleben","year":"2024","unstructured":"Freiesleben T, K\u00f6nig G, Molnar C, Tejero-Cantero A (2024) Scientific inference with interpretable machine learning: analyzing models to learn about real-world phenomena. Mind Mach 34(3):32","journal-title":"Mind Mach"},{"issue":"1","key":"964_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2594473.2594475","volume":"15","author":"AA Freitas","year":"2014","unstructured":"Freitas AA (2014) Comprehensible classification models: a position paper. ACM SIGKDD Explor Newsl 15(1):1\u201310","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"964_CR30","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189\u20131232","journal-title":"Ann Stat"},{"key":"964_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-024-06543-w","volume":"113","author":"C Glanois","year":"2024","unstructured":"Glanois C, Weng P, Zimmer M, Li D, Yang T, Hao J, Liu W (2024) A survey on interpretable reinforcement learning. Mach Learn 113:1\u201344","journal-title":"Mach Learn"},{"issue":"5","key":"964_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D (2018) A survey of methods for explaining black box models. ACM Comput Surv 51(5):1\u201342","journal-title":"ACM Comput Surv"},{"issue":"4","key":"964_CR33","doi-asserted-by":"publisher","DOI":"10.1002\/ail2.61","volume":"2","author":"D Gunning","year":"2021","unstructured":"Gunning D, Vorm E, Wang JY, Turek M (2021) DARPA\u2019s explainable AI (XAI) program: a retrospective. Appl AI Lett 2(4):e61","journal-title":"Appl AI Lett"},{"key":"964_CR34","unstructured":"Heinrich B, Krapf T, Miethaner P (2024) Explore: a novel method for local explanations. In: Proceedings of the 45th international conference on information systems (ICIS)"},{"issue":"7866","key":"964_CR35","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1038\/s41586-021-03659-0","volume":"595","author":"JM Hofman","year":"2021","unstructured":"Hofman JM, Watts DJ, Athey S, Garip F, Griffiths TL, Kleinberg J, Margetts H, Mullainathan S, Salganik MJ, Vazire S, Vespignani A, Yarkoni T (2021) Integrating explanation and prediction in computational social science. Nature 595(7866):181\u2013188","journal-title":"Nature"},{"key":"964_CR36","doi-asserted-by":"crossref","unstructured":"Hohman F, Head A, Caruana R, DeLine R, Drucker SM (2019) Gamut: a design probe to understand how data scientists understand machine learning models. In: Proceedings of the 2019 CHI conference on human factors in computing systems, pp 1\u201313","DOI":"10.1145\/3290605.3300809"},{"key":"964_CR37","unstructured":"Hu X, Rudin C, Seltzer M (2019) Optimal sparse decision trees. In: Proceedings of the 33rd international conference on neural information processing systems, pp 7267\u20137275"},{"issue":"3","key":"964_CR38","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","volume":"31","author":"C Janiesch","year":"2021","unstructured":"Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Mark 31(3):685\u2013695","journal-title":"Electron Mark"},{"issue":"3","key":"964_CR39","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.25300\/MISQ\/2022\/17141","volume":"47","author":"BR Kim","year":"2023","unstructured":"Kim BR, Srinivasan K, Kong SH, Kim JH, Shin CS, Ram S (2023) ROLEX: a novel method for interpretable machine learning using robust local explanations. Manag Inf Syst Q 47(3):1303\u20131332","journal-title":"Manag Inf Syst Q"},{"issue":"2","key":"964_CR40","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.ejor.2023.06.032","volume":"317","author":"M Kraus","year":"2024","unstructured":"Kraus M, Tschernutter D, Weinzierl S, Zschech P (2024) Interpretable generalized additive neural networks. Eur J Oper Res 317(2):303\u2013316","journal-title":"Eur J Oper Res"},{"key":"964_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s12599-024-00922-2","author":"S Kruschel","year":"2025","unstructured":"Kruschel S, Hambauer N, Weinzierl S, Zilker S, Kraus M, Zschech P (2025) Challenging the performance-interpretability trade-off: an evaluation of interpretable machine learning models. Bus Inf Syst Eng. https:\/\/doi.org\/10.1007\/s12599-024-00922-2","journal-title":"Bus Inf Syst Eng"},{"key":"964_CR42","unstructured":"Laugel T, Renard X, Lesot MJ, Marsala C, Detyniecki M (2018) Defining locality for surrogates in post-hoc interpretablity. arXiv:1806.07498"},{"key":"964_CR43","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444","journal-title":"Nature"},{"key":"964_CR44","doi-asserted-by":"crossref","unstructured":"Li O, Liu H, Chen C, Rudin C (2018) Deep learning for case-based reasoning through prototypes: a neural network that explains its predictions. In: Proceedings of the AAAI conference on artificial intelligence. AAAI Press, pp 3530\u20133537","DOI":"10.1609\/aaai.v32i1.11771"},{"issue":"3","key":"964_CR45","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton ZC (2018) The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3):31\u201357","journal-title":"Queue"},{"key":"964_CR46","unstructured":"Liu Z, Wang Y, Vaidya S, Ruehle F, Halverson J, Solja\u010di\u0107 M, Hou TY, Tegmark M (2024) Kan: Kolmogorov\u2013Arnold networks. arXiv:2404.19756"},{"key":"964_CR47","doi-asserted-by":"crossref","unstructured":"Longo L, Brcic M, Cabitza F, Choi J, Confalonieri R, Ser JD, Guidotti R, Hayashi Y, Herrera F, Holzinger A, Jiang R, Khosravi H, Lecue F, Malgieri G, P\u00e1ez A, Samek W, Schneider J, Speith T, Stumpf S (2024) Explainable artificial intelligence (XAI) 2.0: a manifesto of open challenges and interdisciplinary research directions. Inf Fusion 106:102,301","DOI":"10.1016\/j.inffus.2024.102301"},{"issue":"1","key":"964_CR48","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee SI (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2(1):56\u201367","journal-title":"Nat Mach Intell"},{"key":"964_CR49","unstructured":"Luo H, Specia L (2024) From understanding to utilization: a survey on explainability for large language models. arXiv:2401.12874"},{"key":"964_CR50","doi-asserted-by":"crossref","unstructured":"Martens D, Hinns J, Dams C, Vergouwen M, Evgeniou T (2025) Tell me a story! Narrative-driven XAI with Large Language Models. Decis Support Syst 114402","DOI":"10.1016\/j.dss.2025.114402"},{"issue":"1","key":"964_CR51","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1080\/10580530.2020.1849465","volume":"39","author":"C Meske","year":"2022","unstructured":"Meske C, Bunde E, Schneider J, Gersch M (2022) Explainable artificial intelligence: objectives, stakeholders, and future research opportunities. Inf Syst Manag 39(1):53\u201363","journal-title":"Inf Syst Manag"},{"key":"964_CR52","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 (2019) Explanation in artificial intelligence: insights from the social sciences. Artif Intell 267:1\u201338","journal-title":"Artif Intell"},{"key":"964_CR53","unstructured":"Molnar C (2025) Interpretable machine learning, 3rd edn. Lulu.com. https:\/\/christophm.github.io\/interpretable-ml-book. Accessed 15 August 2025"},{"key":"964_CR54","doi-asserted-by":"crossref","unstructured":"Molnar C, Casalicchio G, Bischl B (2020) Interpretable machine learning \u2013 a brief history, state-of-the-art and challenges. In: Joint European conference on machine learning and knowledge discovery in databases, pp 417\u2013431","DOI":"10.1007\/978-3-030-65965-3_28"},{"key":"964_CR55","doi-asserted-by":"crossref","unstructured":"Molnar C, K\u00f6nig G, Herbinger J, Freiesleben T, Dandl S, Scholbeck CA, Casalicchio G, Grosse-Wentrup M, Bischl B (2022) General pitfalls of model-agnostic interpretation methods for machine learning models. In: xxAI \u2013 Beyond explainable AI: international workshop. Springer, pp 39\u201368","DOI":"10.1007\/978-3-031-04083-2_4"},{"issue":"5","key":"964_CR56","doi-asserted-by":"publisher","first-page":"2903","DOI":"10.1007\/s10618-022-00901-9","volume":"38","author":"C Molnar","year":"2024","unstructured":"Molnar C, K\u00f6nig G, Bischl B, Casalicchio G (2024) Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. Data Min Knowl Disc 38(5):2903\u20132941","journal-title":"Data Min Knowl Disc"},{"key":"964_CR57","doi-asserted-by":"crossref","unstructured":"Mothilal RK, Sharma A, Tan C (2020) Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 607\u2013617","DOI":"10.1145\/3351095.3372850"},{"issue":"6","key":"964_CR58","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s12599-023-00806-x","volume":"65","author":"N Pfeuffer","year":"2023","unstructured":"Pfeuffer N, Baum L, Stammer W, Abdel-Karim BM, Schramowski P, Bucher AM, H\u00fcgel C, Rohde G, Kersting K, Hinz O (2023) Explanatory interactive machine learning: establishing an action design research process for machine learning projects. Bus Inf Syst Eng 65(6):677\u2013701","journal-title":"Bus Inf Syst Eng"},{"key":"964_CR59","unstructured":"Press G (2016) Cleaning big data: most time-consuming, least enjoyable data science task, survey says. Forbes, March 23:15"},{"issue":"4","key":"964_CR60","first-page":"953","volume":"24","author":"L Pumplun","year":"2023","unstructured":"Pumplun L, Peters F, Gawlitza JF, Buxmann P (2023) Bringing machine learning systems into clinical practice: a design science approach to explainable machine learning-based clinical decision support systems. J Assoc Inf Syst 24(4):953\u2013979","journal-title":"J Assoc Inf Syst"},{"key":"964_CR61","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \"Why should I trust you?\": Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"key":"964_CR62","unstructured":"Rosenberger J, Schr\u00f6ppel P, Kruschel S, Kraus M, Zschech P, F\u00f6rster M (2025) Navigating the Rashomon effect: How personalization can help adjust interpretable machine learning models to individual users. In: Proceedings of the 33rd European conference on information systems (ECIS), Amman, Jordan"},{"issue":"5","key":"964_CR63","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206\u2013215","journal-title":"Nat Mach Intell"},{"key":"964_CR64","doi-asserted-by":"crossref","unstructured":"Rudin C, Radin J (2019) Why are we using black box models in AI when we don\u2019t need to? A lesson from an explainable AI competition. Harv Data Sci Rev 1(2)","DOI":"10.1162\/99608f92.5a8a3a3d"},{"key":"964_CR65","doi-asserted-by":"crossref","unstructured":"Saphra N, Wiegreffe S (2024) Mechanistic? arXiv:2410.09087","DOI":"10.18653\/v1\/2024.blackboxnlp-1.30"},{"issue":"11","key":"964_CR66","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s10462-024-10916-x","volume":"57","author":"J Schneider","year":"2024","unstructured":"Schneider J (2024) Explainable generative AI (GenXAI): a survey, conceptualization, and research agenda. Artif Intell Rev 57(11):289","journal-title":"Artif Intell Rev"},{"issue":"1","key":"964_CR67","first-page":"8","volume":"52","author":"T Schoormann","year":"2023","unstructured":"Schoormann T, Strobel G, M\u00f6ller F, Petrik D, Zschech P (2023) Artificial intelligence for sustainability \u2013 a systematic review of information systems literature. Commun Assoc Inf Syst 52(1):8","journal-title":"Commun Assoc Inf Syst"},{"key":"964_CR68","unstructured":"Schr\u00f6ppel P, F\u00f6rster M (2024) Exploring XAI users\u2019 needs: a novel approach to personalize explanations using contextual bandits. In: Proceedings of the 32nd European conference on information systems"},{"key":"964_CR69","unstructured":"Shen H, Zeng C, Wang J, Wang Q (2024) Reduced effectiveness of Kolmogorov\u2013Arnold networks on functions with noise. arXiv:2407.14882"},{"key":"964_CR70","doi-asserted-by":"crossref","unstructured":"Siems J, Ditschuneit K, Ripken W, Lindborg A, Schambach M, Otterbach J, Genzel M (2023) Curve your enthusiasm: concurvity regularization in differentiable generalized additive models. In: Proceedings of the 37th conference on neural information processing systems, vol\u00a036, pp 19,029\u201319,057","DOI":"10.52202\/075280-0834"},{"issue":"8","key":"964_CR71","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1038\/s42256-023-00692-8","volume":"5","author":"D Slack","year":"2023","unstructured":"Slack D, Krishna S, Lakkaraju H, Singh S (2023) Explaining machine learning models with interactive natural language conversations using TalkToModel. Nat Mach Intell 5(8):873\u2013883","journal-title":"Nat Mach Intell"},{"key":"964_CR72","doi-asserted-by":"crossref","unstructured":"Sunyaev A, Benlian A, Pfeiffer J, Jussupow E, Thiebes S, Maedche A, Gawlitza J (2025) High-risk artificial intelligence. Bus Inf Syst Eng 1\u201314","DOI":"10.1007\/s12599-025-00942-6"},{"issue":"150","key":"964_CR73","first-page":"1","volume":"20","author":"B Ustun","year":"2019","unstructured":"Ustun B, Rudin C (2019) Learning optimized risk scores. J Mach Learn Res 20(150):1\u201375","journal-title":"J Mach Learn Res"},{"issue":"1","key":"964_CR74","first-page":"614","volume":"35","author":"L Rueden","year":"2021","unstructured":"Rueden L, Mayer S, Beckh K, Georgiev B, Giesselbach S, Heese R, Kirsch B, Pfrommer J, Pick A, Ramamurthy R et al (2021) Informed machine learning \u2013 a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Trans Knowl Data Eng 35(1):614\u2013633","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"964_CR75","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s10940-022-09545-w","volume":"39","author":"C Wang","year":"2023","unstructured":"Wang C, Han B, Patel B, Rudin C (2023) In pursuit of interpretable, fair and accurate machine learning for criminal recidivism prediction. J Quant Criminol 39(2):519\u2013581","journal-title":"J Quant Criminol"},{"key":"964_CR76","doi-asserted-by":"crossref","unstructured":"Wang Y, Xu M, Zhao L (2025) Exploring the cognitive mechanisms behind the adoption of algorithmic advice. Bus Inf Syst Eng 1\u201325","DOI":"10.1007\/s12599-025-00925-7"},{"key":"964_CR77","doi-asserted-by":"crossref","unstructured":"Watanabe A, Kuramata M, Majima K, Kiyohara H, Kensho K, Nakata K (2021) Constrained generalized additive 2 model with consideration of high-order interactions. In: 2021 International conference on electrical, computer and energy technologies, pp 1\u20136","DOI":"10.1109\/ICECET52533.2021.9698779"},{"key":"964_CR78","doi-asserted-by":"crossref","unstructured":"Weinzierl S, Zilker S, Zschech P, Kraus M, Leibelt T, Matzner M (2024) How risky is my AI system? A method for transparent classification of AI system descriptions by regulated AI risk categories. In: Proceedings of the 45th international conference on information systems (ICIS)","DOI":"10.2139\/ssrn.4984202"},{"issue":"108","key":"964_CR79","first-page":"192","volume":"120","author":"Z Yang","year":"2021","unstructured":"Yang Z, Zhang A, Sudjianto A (2021) GAMI-Net: an explainable neural network based on generalized additive models with structured interactions. Pattern Recogn 120(108):192","journal-title":"Pattern Recogn"},{"issue":"2","key":"964_CR80","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3639372","volume":"15","author":"H Zhao","year":"2024","unstructured":"Zhao H, Chen H, Yang F, Liu N, Deng H, Cai H, Wang S, Yin D, Du M (2024) Explainability for large language models: a survey. ACM Trans Intell Syst Technol 15(2):1\u201338","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"2","key":"964_CR81","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s10729-024-09673-8","volume":"27","author":"S Zilker","year":"2024","unstructured":"Zilker S, Weinzierl S, Kraus M, Zschech P, Matzner M (2024) A machine learning framework for interpretable predictions in patient pathways: the case of predicting ICU admission for patients with symptoms of sepsis. Health Care Manag Sci 27(2):136\u2013167","journal-title":"Health Care Manag Sci"}],"container-title":["Business &amp; Information Systems Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12599-025-00964-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12599-025-00964-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12599-025-00964-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T07:03:31Z","timestamp":1776668611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12599-025-00964-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"references-count":81,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["964"],"URL":"https:\/\/doi.org\/10.1007\/s12599-025-00964-0","relation":{},"ISSN":["2363-7005","1867-0202"],"issn-type":[{"value":"2363-7005","type":"print"},{"value":"1867-0202","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,15]]},"assertion":[{"value":"17 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}