Sources of Inequity and Fairness Risks inWellbeing Sensing

2026-07-23

AI Summary

This study addresses fairness risks in passive sensing systems for mental health monitoring that extend beyond traditional identity-based dimensions, arising from heterogeneous sensing modalities, indirect behavioral inference, and long-term deployment. Through semi-structured interviews with 14 researchers and practitioners across five countries, the work integrates human-computer interaction and fair AI frameworks to systematically analyze sociotechnical factors throughout the system lifecycle. It identifies five contextual sources of inequality—such as disparities in surveillance acceptability and behavioral regularity—and proposes 15 risk-mitigation strategies spanning design to deployment phases, uncovering underlying structural barriers. The findings offer multi-level governance recommendations for researchers, funders, and deploying institutions to advance more equitable well-being sensing systems.

Abstract

Passive sensing for wellbeing uses smartphones and wearables to continuously collect human behavioral data and applies ML/AI models to infer psychological states and behaviors (e.g., depression, cognitive load). These systems are increasingly adopted in high-stakes settings (e.g., hospitals, universities), yet fairness research remains limited---primarily to post-hoc, identity-based comparisons of model performance. However, passive sensing combines heterogeneous sensing infrastructures, indirect behavioral inference, and longitudinal deployment---characteristics that, while not exclusive to the domain, are jointly pronounced here and raise two underexplored questions: (1) what additional sources of inequity arise from these characteristics, and (2) how do such inequities propagate beyond algorithmic audits across the system lifecycle? To address this gap, we conducted semi-structured interviews with 14 researchers and practitioners across five countries, examining how fairness risks emerge and are negotiated across the full passive sensing lifecycle. Our findings empirically characterize five situated sources of inequity (e.g., comfort with monitoring, behavioral regularity) that systematically shape fairness risks beyond identity-based attributes. We further synthesize 15 fairness risks and corresponding mitigation strategies across the lifecycle, from study design to deployment. Finally, we identify structural barriers that constrain fair practice in reality, and argue that enabling fair passive sensing requires both individual researcher efforts and ecosystem-level governance support from funders, publication venues, and deploying institutions.

Problem

• fairness
• passive sensing
• wellbeing
• inequity
• AI ethics

Innovation

• passive sensing
• fairness risks
• wellbeing monitoring
• lifecycle equity
• situational inequity