Illuminating the Unseen: Investigating the Context-induced Harms in Behavioral Sensing

TL;DR

This paper presents a new framework for evaluating harms in behavioral sensing technologies, emphasizing context-specific identities and deployment settings, demonstrated through real-world case studies across diverse international contexts.

Contribution

It introduces a domain-specific, iterative harm evaluation framework tailored for behavioral sensing, addressing gaps in existing generic ML harm assessment methods.

Findings

Empirical evidence of identity-based and domain-specific harms.

Demonstrated the framework's applicability across different contexts.

Analyzed trade-offs of bias mitigation techniques.

Abstract

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.