Behavioral sensing technologies are rapidly evolving across a range of well-being applications. Despite its potential, concerns about the responsible use of such technology are escalating. In response, recent research within the sensing technology has started to address these issues. While promising, they primarily focus on broad demographic categories and overlook more nuanced, context-specific identities. These approaches lack grounding within domain-specific harms that arise from deploying sensing technology in diverse social, environmental, and technological settings. Additionally, existing frameworks for evaluating harms are designed for a generic ML life cycle, and fail to adapt to the dynamic and longitudinal considerations for behavioral sensing technology. To address these gaps, we introduce a framework specifically designed for evaluating behavioral sensing technologies. This framework emphasizes a comprehensive understanding of context, particularly the situated identities of users and the deployment settings of the sensing technology. It also highlights the necessity for iterative harm mitigation and continuous maintenance to adapt to the evolving nature of technology and its use. We demonstrate the feasibility and generalizability of our framework through post-hoc evaluations on two real-world behavioral sensing studies conducted in different international contexts, involving varied population demographics and machine learning tasks. Our evaluations provide empirical evidence of both situated identity-based harm and more domain-specific harms, and discuss the trade-offs introduced by implementing bias mitigation techniques.

翻译：行为感知技术在各类健康应用领域正迅速发展。尽管其潜力巨大，但对该技术负责任使用的担忧也在日益加剧。为此，感知技术领域的最新研究已开始关注这些问题。这些研究虽具前景，但主要关注广泛的群体类别，忽视了更为微妙的情境特定身份。此类方法缺乏在部署感知技术时因不同社会、环境及技术背景而产生的领域特异性危害根基。此外，现有的危害评估框架针对通用的机器学习生命周期设计，未能适应行为感知技术的动态与纵向考量。为弥补这些空白，我们提出一个专为评估行为感知技术设计的框架。该框架强调对情境的全面理解，特别是用户的情境化身份与感知技术的部署环境。同时，它凸显了迭代式危害缓解与持续性维护的必要性，以适应技术及其用途的演变特性。我们通过在两项不同国际背景下开展的真实世界行为感知研究（涉及不同人口群体与机器学习任务）中进行事后评估，证明了该框架的可行性与泛化能力。这些评估为基于情境身份的危害及更多领域特异性危害提供了经验性证据，并讨论了实施偏差缓解技术所引入的权衡。