[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126643-en":3,"doc-seo-126643-105":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},126643,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","FairComp - Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing","FairComp workshop explores how to make Ubiquitous Computing (UbiComp) research outcomes both ethical and fair as machine learning becomes central to pervasive devices. The program addresses social, technical, and legal implications: examining relationships between fairness and harm or rights, initiating bias-mitigation data practices tailored to UbiComp, and considering how emerging policies shape community work. It aims to build a responsible UbiComp community and chart future research directions.","FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing  \narXiv :2309 . 12877v1 [ cs .CY] 22 Sep 2023  \nSofia Yfantidou  \n[syfantid@csd.auth.gr](syfantid@csd.auth.gr)[ ](syfantid@csd.auth.gr)Aristotle University of Thessaloniki Thessaloniki, Greece  \nDimitris Spathis∗ dimitrios.spathis @[nokia-bell-labs.com](nokia-bell-labs.com)[ ](nokia-bell-labs.com)Nokia Bell Labs Cambridge, United Kingdom  \nMarios Constantinides∗  \nmarios.constantinides @[nokia-bell-labs.com](nokia-bell-labs.com)[ ](nokia-bell-labs.com)Nokia Bell Labs Cambridge, United Kingdom  \nTong Xia  \n[tx229@cam.ac.uk](tx229@cam.ac.uk)[ ](tx229@cam.ac.uk)University of Cambridge Cambridge, United Kingdom  \nABSTRACT  \nHow can we ensure that Ubiquitous Computing(UbiComp)research outcomes are both ethical and fair? While fairness in machine learning (ML) has gained traction in recent years, fairness in UbiComp remains unexplored. This workshop aims to discuss fairness in UbiComp research and its social, technical, and legal implications. From a social perspective, we will examine the relationship between fairness and UbiComp research and identify pathways to ensure that ubiquitous technologies do not cause harm or infringe on individual rights. From a technical perspective, we will initiate a discussion on data practices to develop bias mitigation approaches tailored to UbiComp research. From a legal perspective, we will examine how new policies shape our community’s work and future research. We aim to foster a vibrant community centered around the topic of responsible UbiComp, while also charting a clear path for future research endeavours in this field.  \nCCS CONCEPTS  \n•Human-centeredcomputing → Ubiquitous and mobile computing; • Applied computing → Consumer health; • Social and professional topics → Codes of ethics.  \nKEYWORDS  \nfairness, bias, discrimination, responsible AI, ethical AI  \nACM Reference Format:  \nSofia Yfantidou, DimitrisSpathis, Marios Constantinides, Tong Xia, and Niels van Berkel. 2023. FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing. In Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing (UbiComp/ISWC ’23 Adjunct ), October 8􀀕12, 2023, Cancun, Quintana Roo,  \nNiels van Berkel [nielsvanberkel@cs.aau.dk](nielsvanberkel@cs.aau.dk)  \nAalborg University  \nAalborg, Denmark  \n1 BACKGROUND  \nDue to the integration of Machine Learning (ML) into Ubiquitous Computing (UbiComp), tasks that were once deemed impossible or reserved exclusively for humans, are now within technology’s reach. Algorithms running on ubiquitous devices, such as smartphonesand wearables, have been employed to recognize human activities[4], track sleep patterns[5], and detect breathing phases[10] . Currently, we witness a surge in high-stakes applications such as diagnosing COVID-19 infections [1], detection of Atrial Fibrillation (AFib) [7], and enhancement of cognitive performance [3] . However, as with any technological advancement, ethical opportunities and risks come hand in hand, and, similarly to humans, ML algorithms are susceptible to biases.  \nWhile fairness research has gained popularity in recent years [8, 12], with a dedicated conference and scientific community (FAccT), fairness inUbiComp remains wildly unexplored[14]. Yet, UbiComp applications are equally likely to suffer from biases. For example, health sensors such as oximeters consistently misclassify people of color [11], while client selection in federated learning incorporates biases against user profiles on wearables of inferior networking conditions [15] . Additionally, ubiquitous data and models have certain particularities, oftentimes not shared with the broader scholarly discourse on ML ethics. For example, they typically include small-scale, proof-of-concept datasets collected in the lab, making it difficult to extract population-","cbCail75f7D8r0JF","https://ap.wps.com/l/cbCail75f7D8r0JF","pdf",125708,1,5,"English","en",105,"# Background\n## Fairness in UbiComp as an Underexplored Need\n## Risks and Sources of Bias in UbiComp\n# Workshop Motivation and Goals\n## Social, Technical, and Legal Implications\n## Building a Responsible UbiComp Community","[{\"question\":\"Why does the workshop focus on fairness in UbiComp research?\",\"answer\":\"Because fairness research has grown in recent years, yet fairness in Ubiquitous Computing remains largely unexplored despite the likelihood of biased outcomes in UbiComp applications.\"},{\"question\":\"What kinds of bias issues does the document highlight for UbiComp?\",\"answer\":\"It notes that health sensors can misclassify people of color and that federated learning client selection can embed biases tied to users’ networking conditions, along with UbiComp-specific data and temporal characteristics that make biases harder to surface.\"},{\"question\":\"How does the workshop propose to address fairness and robustness across perspectives?\",\"answer\":\"It plans discussions from social, technical, and legal angles: linking fairness to harm and rights, starting bias-mitigation approaches through tailored data practices, and examining how new policies influence future community research.\"}]","FairComp - Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing | PDF",1785934005,13,{"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},"faircomp-workshop-on-fairness-and-robustness-in-machine-learning-for-ubiquitous-computing","",{"@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/faircomp-workshop-on-fairness-and-robustness-in-machine-learning-for-ubiquitous-computing/126643/",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},"Why does the workshop focus on fairness in UbiComp research?","Question",{"text":75,"@type":76},"Because fairness research has grown in recent years, yet fairness in Ubiquitous Computing remains largely unexplored despite the likelihood of biased outcomes in UbiComp applications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What kinds of bias issues does the document highlight for UbiComp?",{"text":80,"@type":76},"It notes that health sensors can misclassify people of color and that federated learning client selection can embed biases tied to users’ networking conditions, along with UbiComp-specific data and temporal characteristics that make biases harder to surface.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the workshop propose to address fairness and robustness across perspectives?",{"text":84,"@type":76},"It plans discussions from social, technical, and legal angles: linking fairness to harm and rights, starting bias-mitigation approaches through tailored data practices, and examining how new policies influence future community research.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]