Uncovering bias in personal informatics

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• dc.contributor.author Yfantidou, Sofia
• dc.contributor.author Sermpezis, Pavlos
• dc.contributor.author Vakali, Athena
• dc.contributor.author Baeza Yates, Ricardo
• dc.date.accessioned 2025-10-07T06:11:41Z
• dc.date.available 2025-10-07T06:11:41Z
• dc.date.issued 2023
• dc.description.abstract Personal informatics (PI) systems, powered by smartphones and wearables, enable people to lead healthier lifestyles by providing meaningful and actionable insights that break down barriers between users and their health information. Today, such systems are used by billions of users for monitoring not only physical activity and sleep but also vital signs and women's and heart health, among others. Despite their widespread usage, the processing of sensitive PI data may suffer from biases, which may entail practical and ethical implications. In this work, we present the first comprehensive empirical and analytical study of bias in PI systems, including biases in raw data and in the entire machine learning life cycle. We use the most detailed framework to date for exploring the different sources of bias and find that biases exist both in the data generation and the model learning and implementation streams. According to our results, the most affected minority groups are users with health issues, such as diabetes, joint issues, and hypertension, and female users, whose data biases are propagated or even amplified by learning models, while intersectional biases can also be observed.en
• dc.description.sponsorship This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813162. The content of this paper reflects only the authors’ view and the Agency and the Commission are not responsible for any use that may be made of the information it contains. Results presented in this work have been produced using the Aristotle University of Thessaloniki Compute Infrastructure and Resources. The authors would like to acknowledge the support provided by the Scientific Computing Office throughout the progress of this research work.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Yfantidou S, Sermpezis P, Vakali A, Baeza-Yates R. Uncovering bias in personal informatics. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2023 Sep;7(3):1-30. DOI: 10.1145/3610914
• dc.identifier.doi http://dx.doi.org/10.1145/3610914
• dc.identifier.issn 2474-9567
• dc.identifier.uri http://hdl.handle.net/10230/71413
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2023 Sep;7(3):1-30
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/813162
• dc.rights Copyright © 2023 Owner/Author. This work is licensed under a Creative Commons Attribution International 4.0 License.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Personal informaticsen
• dc.subject.keyword Biasen
• dc.subject.keyword Digital biomarkersen
• dc.subject.keyword Fairnessen
• dc.subject.keyword Machine learningen
• dc.subject.keyword Sensing dataen
• dc.subject.keyword Ubiquitous computingen
• dc.title Uncovering bias in personal informaticsen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion