[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126694-en":3,"doc-seo-126694-105":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},126694,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",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 focused on understanding how machine-learning (ML) models work and on ensuring that explanations fit targeted users and real usage contexts. Linking ML concerns with human-computer interaction (HCI) perspectives, the work reports lessons drawn from organizing an XAI workshop during IHM’22 and gathering about thirty researchers. It synthesizes key teachings and promising directions for collaboration between the communities.","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","cbCait5REXSgD1SV","https://ap.wps.com/l/cbCait5REXSgD1SV","pdf",2462816,1,10,"English","en",105,"# Introduction\n## Goals of XAI and transparency\n## Workshop-based contributions","[{\"question\":\"What does XAI aim to achieve in this work?\",\"answer\":\"XAI aims to develop methods and tools to explain how AI systems work in understandable terms, addressing transparency and trust concerns.\"},{\"question\":\"How does the paper connect ML with HCI?\",\"answer\":\"It positions XAI as combining ML model interpretability with the adequacy of explanations for targeted users and usage contexts, which are central HCI concerns.\"},{\"question\":\"What source material did the authors use to derive their main teachings?\",\"answer\":\"The authors summarize lessons from an XAI workshop held during the IHM’22 conference with about thirty researchers from HCI and ML communities.\"}]","Bridging the Gap between Human-Computer Interaction and Machine-Learning on Explainable AI - 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