- Research Article
- 10.1145/3760535.3760541
Uncovering the Hidden Biases in Personal Informatics
- Aug 11, 2025
- GetMobile: Mobile Computing and Communications
- Sofia Yfantidou + 3 more +3
Personal Informatics (PI) systems, such as apps and wearables that help users track physical activity, sleep, heart rate, or stress, have become critical tools for self-monitoring and health research. As these systems increasingly drive personal and clinical decisionmaking, it's vital to understand how equitable and representative they really are. Real-world harms have already surfaced in adjacent domains: health sensors like pulse oximeters underperform on darker skin tones [1], and female speakers and non-US nationalities experience significant performance degradation in automated speaker recognition [2]. These failures aren't just technical - they're structural, human-centric, and societal. Yet, despite their growing influence, PI systems remain critically under-researched from a fairness and equity perspective [3]. Our research, detailed in [4], investigates this question by examining when, how, and for whom bias arises in the lifecycle of PI systems.
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