[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125708-en":3,"doc-seo-125708-105":30,"detail-sidebar-cat-0-en-105":91},{"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":27,"seo_description":14,"update_tm":28,"read_time":29},125708,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Bridging the Gap between Human-Computer Interaction and Machine-Learning on Explainable AI - Initial Observations and Lessons Learned","Explainability in artificial intelligence (XAI) is a fast-growing research area connecting machine-learning (ML) needs with human-computer interaction (HCI) concerns. XAI addresses both the technical ability to understand ML model behavior and the suitability of explanations for target users and application contexts. To capture these interactions, a workshop on XAI was organized during IHM’22, bringing together about thirty researchers from HCI and ML communities. The paper synthesizes key lessons and promising directions for collaboration.","Institutional Repository-Research Portal Dépôt Institutionnel-Portail de la Recherche  \n University of easr[chportal.unamur.be](chportal.unamur.be)  \nRESEARCH OUTPUTS / RÉSULTATS DE RECHERCHE  \nBridging the Gap between Human-Computer Interaction and Machine-Learning on Explainable AI  \nAlbert, Julien; Bibal, Adrien; Frénay, Benoît; Dumas, Bruno  \nPublished in:  \nIHM'23-34e Conférence Internationale Francophone sur l'Interaction Humain-Machine  \nPublication date: 2023  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication  \nCitation for pulished version (HARVARD):  \nAlbert, J, Bibal, A, Frénay, B & Dumas, B 2023, Bridging the Gap between Human-Computer Interaction and Machine-Learning on Explainable AI: Initial Observations and Lessons Learned. in IHM'23-34e Conférence Internationale Francophone sur l'Interaction Humain-Machine.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal ?  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 29. Nov. 2023  \nBridging the Gap between Human-Computer Interaction and Machine-Learning on Explainable AI: Initial Observations and Lessons Learned  \nCombler la distance entre l’interaction humain-machine et le machine learning sur l’IA explicable : premières observations et leçons apprises  \nJULIEN ALBERT, NaDI/PReCISE, Faculty of Computer Science, University of Namur, Belgium ADRIEN BIBAL, University of Colorado Anschutz Medical Campus, USA  \nBENOÎT FRENAY, NaDI/PReCISE, Faculty of Computer Science, University of Namur, Belgium BRUNO DUMAS, NaDI/PReCISE, Faculty of Computer Science, University of Namur, Belgium  \nExplainability in artificial intelligence (XAI) is a rapidly growing research field nowadays, in particular in machine learning (ML) . XAI concerns both the technical capacity to understand the functioning of ML models and the adequacy of the explanations with the targeted users and the contexts of use. It thus joins both the concerns of ML and of human-computer interaction (HCI) researchers. We therefore organized a workshop on XAI during the IHM’22 conference and gathered about thirty researchers from the HCI and ML communities. The contribution of this paper sums up the main teachings and the most promising avenues for collaboration that emerged during the discussions.  \nCCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Machine learning.  \nAdditional Key Words and Phrases: explainable artificial intelligence, machine learning, human-computer interaction L’explicabilité en intelligence artificielle (XAI) est un domaine de recherche en plein essor aujourd’hui, en particulier en machine learning (ML) . Ce domaine concerne à la fois la capacité technique à comprendre le fonctionnement des modèles de ML et l’adéquation des explications avec les utilisateurs et les contextes d’utilisation ciblés. Il rejoint ainsi les préoccupations des chercheurs à la fois en ML et en interaction humain-machine (IHM) . Nous avons donc organisé un atelier sur l’XAI pendant la conférence IHM’22 et avonsréuni une trentaine de chercheurs issus des communautés IHM et ML. La contribution de ce présent article résume les principaux enseignements et les pistes de collaboration les plus prometteuses qui ont émergé l","cbCaikewMXEZXTSd","https://ap.wps.com/l/cbCaikewMXEZXTSd","pdf",2462816,1,10,"English","en",105,"# Introduction\n## XAI goals and motivations\n## Workshop contribution and collaboration direction","[{\"question\":\"What does explainable AI (XAI) aim to achieve in this work?\",\"answer\":\"It develops methods and tools to explain AI systems in understandable terms for humans. It also targets transparency and trust concerns.\"},{\"question\":\"Why is XAI positioned at the intersection of ML and HCI?\",\"answer\":\"XAI covers both understanding how ML models work and ensuring explanations match users and the contexts where they are used.\"},{\"question\":\"How did the authors gather insights for their paper?\",\"answer\":\"They organized an XAI workshop during IHM’22 and brought together about thirty researchers from the HCI and ML communities.\"}]","Bridging the Gap between Human-Computer Interaction and Machine-Learning on Explainable AI - Initial Observations and Lessons Learned | PDF",1785900758,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"bridging-the-gap-between-human-computer-interaction-and-machine-learning-on-explainable-ai-initial-observations-and-lessons-learned","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/bridging-the-gap-between-human-computer-interaction-and-machine-learning-on-explainable-ai-initial-observations-and-lessons-learned/125708/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does explainable AI (XAI) aim to achieve in this work?","Question",{"text":75,"@type":76},"It develops methods and tools to explain AI systems in understandable terms for humans. 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