Foundations for fairness in digital health apps

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• dc.contributor.author Buda, Teodora Sandra
• dc.contributor.author Guerreiro, João
• dc.contributor.author Omana Iglesias, Jesus
• dc.contributor.author Castillo, Carlos
• dc.contributor.author Smith, Oliver
• dc.contributor.author Matic, Aleksandar
• dc.date.accessioned 2025-10-21T05:48:19Z
• dc.date.available 2025-10-21T05:48:19Z
• dc.date.issued 2022
• dc.description.abstract Digital mental health applications promise scalable and cost-effective solutions to mitigate the gap between the demand and supply of mental healthcare services. However, very little attention is paid on differential impact and potential discrimination in digital mental health services with respect to different sensitive user groups (e.g., race, age, gender, ethnicity, socio-economic status) as the extant literature as well as the market lack the corresponding evidence. In this paper, we outline a 7-step model to assess algorithmic discrimination in digital mental health services, focusing on algorithmic bias assessment and differential impact. We conduct a pilot analysis with 610 users of the model applied on a digital wellbeing service called Foundations that incorporates a rich set of 150 proposed activities designed to increase wellbeing and reduce stress. We further apply the 7-step model on the evaluation of two algorithms that could extend the current service: monitoring step-up model, and a popularity-based activities recommender system. This study applies an algorithmic fairness analysis framework for digital mental health and explores differences in the outcome metrics for the interventions, monitoring model, and recommender engine for the users of different age, gender, type of work, country of residence, employment status and monthly income.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Buda TS, Guerreiro J, Omana J, Castillo C, Smith O, Matic A. Foundations for fairness in digital health apps. Front Digit Health. 2022 Aug 30;4:943514. DOI: 10.3389/fdgth.2022.943514
• dc.identifier.doi http://dx.doi.org/10.3389/fdgth.2022.943514
• dc.identifier.issn 2673-253X
• dc.identifier.uri http://hdl.handle.net/10230/71602
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in Digital Health. 2022 Aug 30;4:943514
• dc.rights © 2022 Buda, Guerreiro, Omana Iglesias, Castillo, Smith and Matic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Digital mental healthen
• dc.subject.keyword Algorithmic fairnessen
• dc.subject.keyword Case studyen
• dc.subject.keyword Undesired biasen
• dc.subject.keyword Algorithmic discriminationen
• dc.title Foundations for fairness in digital health appsen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion