AI 中文总结
研究幸福感被动感知中公平性问题，通过对五国14人半结构化访谈，实证描述五个情境化不公平来源，综合十五种公平风险及缓解策略，指出实现公平被动感知需个人努力与生态系统治理支持。
AI 中文摘要
幸福感的被动感知利用智能手机和可穿戴设备持续收集人类行为数据，并应用机器学习/人工智能模型来推断心理状态和行为（如抑郁、认知负荷）。这些系统在高风险环境（如医院、大学）中越来越多地被采用，但公平性研究仍然有限，主要是事后基于身份的模型性能比较。被动感知结合了异构传感基础设施、间接行为推理和纵向部署，这些特性引发了两个未被充分探索的问题：这些特性会产生哪些额外的不公平来源，以及这些不公平如何在系统生命周期中超越算法审计进行传播？为了解决这一差距，我们对五个国家的14名研究人员和从业者进行了半结构化访谈，研究公平风险如何在整个被动感知生命周期中出现和协商。我们的发现从实证角度描述了五个情境化的不公平来源，这些来源系统地塑造了基于身份属性之外的公平风险。我们还综合了从研究设计到部署的整个生命周期中的十五种公平风险及相应的缓解策略。最后，我们确定了限制现实中公平实践的结构性障碍，并认为实现公平的被动感知既需要研究人员的个人努力，也需要资助者、出版机构和部署机构在生态系统层面的治理支持。
英文摘要
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.
Comments16 pages, 1 figure, 2 tables. Accepted to AIES 2026
