  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.
