[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124094-en":3,"doc-seo-124094-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124094,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Explaining complex systems - a tutorial on transparency and interpretability in machine learning models (part I)","This tutorial provides a foundational entry point to eXplainable AI (XAI) for confronting the black-box challenge in machine learning. It motivates researchers in systems theory to engage with data-driven methods increasingly used in control design, where understanding model decisions becomes essential for safe and reliable deployment. Linking XAI with system identification, the tutorial presents methodologies that illuminate underlying decision processes and improve interpretability. It emphasizes quantitative approaches, starting with local explanations and progressing to global perspectives including counterfactuals, physics-informed AI, and equation discovery.","Explaining complex systems  \nCitation for published version (APA):  \nMaterassi, D. , Warnick, S. , Rojas, C. , Schoukens, M. , & Cross, E. (2024) . Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part I) . IFAC-PapersOnLine , 58(15), 492-496. [https://doi.org/10.1016/j.ifacol.2024.08.577](https://doi.org/10.1016/j.ifacol.2024.08.577)  \nDocument license:  \nCC BY-NC-ND  \nDOI:  \n10.1016/j.ifacol.2024.08.577  \nDocument status and date:  \nPublished: 01/07/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. 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Feb. 2025  \n[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nIFAC PapersOnLine 58-15 (2024) 492–496  \nExplaining complex systems: a tutorial on transparency and interpretability in machine learning models (part I)  \nDonatello Materassi ∗ Sean Warnick ∗∗ Cristian Rojas ∗∗∗ Maarten Schoukens ∗∗∗∗ Elizabeth Cross †  \n∗ Department of Electrical and Computer Engineering, University of  \nMinnesota, [mater013@umn.edu](mater013@umn.edu)  \n∗∗ US Department of Homeland Security, Science and Technology  \nDirectorate, [sean.warnick@hq.dhs.gov](sean.warnick@hq.dhs.gov)  \n∗∗∗ EECS / Division of Decision and Control Systems, KTH Royal  \nInstitute of Technology, [cristian.rojas@ee.kth.se](cristian.rojas@ee.kth.se)[ ](cristian.rojas@ee.kth.se)∗∗∗∗ Control Systems Group, Eindhoven University of Technology,  \nEindhoven, The Netherlands, [m.schoukens@tue.nl](m.schoukens@tue.nl)[ ](m.schoukens@tue.nl)† Department of Mechanical Engineering, University of Sheffield,  \n[e.j.cross@sheffield.ac.uk](e.j.cross@sheffield.ac.uk)  \nAbstract: This tutorial seeks to serve as a foundational entry point for eXplainable AI (XAI) as a tool to address the inherent black box challenge associated with many machine learning approaches. Also, it is designed to encourage researchers in systems theory to actively engage with the increasing integration of data-driven methods in control design. Indeed, with AI becoming more and more pervasive, understanding the decisions made by these sophisticated models is becoming paramount. The tutorial’s motivation stems from the realization that traditional ","cbCaihlrrFi3kqlt","https://ap.wps.com/l/cbCaihlrrFi3kqlt","pdf",519685,1,"English","en",105,"# Abstract\n# Introduction\n## eXplainable AI for black-box machine learning\n## Transparency and interpretability goals\n# Tutorial scope (Part I)\n## Local explanations and relation to system identification\n## Global explanations and advanced directions","[{\"question\":\"What problem does the tutorial address?\",\"answer\":\"It targets the black-box nature of many machine learning models by using eXplainable AI to make decisions more transparent and interpretable.\"},{\"question\":\"How does the tutorial connect XAI with systems theory or control design?\",\"answer\":\"It encourages systems-theory researchers to engage with data-driven control design by explaining how XAI supports safe, reliable deployment through better understanding of model decisions, and by linking XAI to system identification.\"},{\"question\":\"What types of explanation methods does Part I cover?\",\"answer\":\"It first describes local explanation methods that explain model outputs for specific inputs, then moves toward more global perspectives such as counterfactual explanations, physics-informed AI, and equation discovery.\"}]","Explaining complex systems - 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