[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117280-en":3,"doc-seo-117280-105":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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117280,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Explanatory Interactive Machine Learning - Establishing an Action Design Research Process for Machine Learning Projects","Most effective standard machine learning achieves strong classification accuracy, yet humans struggle to understand the rationale behind black-box outcomes, limiting knowledge creation and organizational trust. Explainable AI improves interpretability, while Interactive Machine Learning embeds humans into insight discovery. The paper combines both directions by proposing Explanatory Interactive Machine Learning (XIL) integrated into a generalizable Action Design Research (ADR) workflow (XIL-ADR) for analyzing data, inspecting models, and iteratively improving them.","Bus Inf Syst Eng 65(6):677–701 (2023)  \n[https://doi.org/10.1007/s12599-023-00806-x](https://doi.org/10.1007/s12599-023-00806-x)  \nRESEARCH PAPER  \nExplanatory Interactive Machine Learning  \nEstablishing an Action Design Research Process for Machine Learning Projects  \nNicolas Pfeuffer • Lorenz Baum • Wolfgang Stammer • Benjamin M. Abdel-Karim • Patrick Schramowski • Andreas M. Bucher • Christian H¨ugel • Gernot Rohde • Kristian Kersting • Oliver Hinz  \nReceived: 15 January 2022/Accepted: 17 January 2023/Published online: 21 April 2023  \n􀀂 The Author(s) 2023  \nAbstract The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cuttingedge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XILADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the  \nAccepted after two revisions by Oscar Pastor.  \nN. Pfeuffer 􀀂 L. Baum 􀀂 B. M. Abdel-Karim 􀀂 O. Hinz (&) Information Systems and Information Management, Goethe University Frankfurt, Frankfurt am Main, Germany  \ne-mail: [ohinz@wiwi.uni-frankfurt.de](ohinz@wiwi.uni-frankfurt.de)  \n[W. Stammer](W. Stammer) 􀀂 P. Schramowski 􀀂 K. Kersting  \nMachine Learning Group, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany  \nA. M. Bucher  \nDiagnostic and Interventional Radiology, Center of Radiology, Hospital of the Goethe University Frankfurt, Frankfurt am Main, Germany  \nC. H¨ugel 􀀂 G. Rohde  \nPneumology and Allergology, Center of Internal Medicine, Hospital of the Goethe University Frankfurt, Frankfurt am Main, Germany  \napplication of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AIbased systems for organizations and research.  \nKeywords Action design research 􀀂 Data science 􀀂 Explainable artificial intelligence 􀀂 Interactive machine learning 􀀂 Pneumonia 􀀂 Corona virus  \n1 Introduction  \nIncreasingly, it is becoming apparent that Interactive Machine Learning (IML), i.e., the integration of user feedback into a Machine Learning (ML) process to modify an ML model (e.g., Amershi et al. 2015), may play a leading role in shaping Artificial Intelligence (AI) and in particular ML-based systems for effective use in organizations. To realize their full potential, AI systems must become capable of communicating and collaborating with, learning from, and teaching their users. Finding the right ways to induce learning in human-machine interaction is required to unlock many scientific and commercial opportunities in AI (Teso and Hinz 2020) .  \nChallenges to thorough understanding, potential learning from AI-based Systems, and effective organizational use result from the lack of system transparency (e.g., Rai 2020), which is often accompanied by the uncertainty of whether a system is biased. In the past five years, various scholars have shown the potential adverse effects of algorithmic biases on human decision-making (e.g., Lambrecht and Tucker 2019) . Outcries demanding transparency and  \naccountability have become louder and, as such, have found their wa","cbCaiu78AjDFah2I","https://ap.wps.com/l/cbCaiu78AjDFah2I","pdf",5317554,1,25,"English","en",105,"# Introduction\n## Interactive Machine Learning and organizational use\n## Transparency, bias, and regulatory pressure\n## Data science processes and recursion\n## Human-in-the-loop and the need for explanations\n## Explanatory interactive machine learning (XIL)","[{\"question\":\"Why is explainable or interactive machine learning needed in practice?\",\"answer\":\"Standard ML can be accurate but remains opaque, making it hard for humans to understand decisions and create new knowledge. This opacity also complicates trust and responsible organizational use when bias or errors are a concern.\"},{\"question\":\"What is Explanatory Interactive Machine Learning (XIL) in this paper?\",\"answer\":\"XIL merges explainable AI with interactive machine learning so that model results become more interpretable and humans can participate in insight discovery. The goal is to support iterative learning with human understanding.\"},{\"question\":\"How does the proposed XIL-ADR process work for machine learning projects?\",\"answer\":\"The paper embeds XIL into a generalizable Action Design Research (ADR) process. It enables analysis of data, inspection of models, and iterative improvement, while helping identify shortcomings of standard ML projects.\"}]",1785674969,63,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"explanatory-interactive-machine-learning-establishing-an-action-design-research-process-for-machine-learning-projects","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/explanatory-interactive-machine-learning-establishing-an-action-design-research-process-for-machine-learning-projects/117280/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is explainable or interactive machine learning needed in practice?","Question",{"text":74,"@type":75},"Standard ML can be accurate but remains opaque, making it hard for humans to understand decisions and create new knowledge. This opacity also complicates trust and responsible organizational use when bias or errors are a concern.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What is Explanatory Interactive Machine Learning (XIL) in this paper?",{"text":79,"@type":75},"XIL merges explainable AI with interactive machine learning so that model results become more interpretable and humans can participate in insight discovery. The goal is to support iterative learning with human understanding.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the proposed XIL-ADR process work for machine learning projects?",{"text":83,"@type":75},"The paper embeds XIL into a generalizable Action Design Research (ADR) process. 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