On Being Told How We Feel

Algorithms and sensors are increasingly deployed for highly personal aspects of our everyday lives. Recent work suggests people have imperfect understanding of system outputs, often assuming sophisticated capabilities and deferring to feedback. We explore how people construe algorithmic interpretations of emotional data in personal informatics systems. A survey (n=188) showed strong interest in automatic stress and emotion tracking, but many respondents expected these systems to provide objective measurements for their emotional experiences. A second study examined how algorithmic sensor feedback influences emotional self-judgments, by comparing three system framings of physiological ElectroDermal Activity data (EDA): Positive ("alert and engaged"), Negative ("stressed"), and Control (no frame) in a mixed-methods study with 64 participants. Despite users reporting strategies to test system outputs, users still deferred to feedback and their perceived emotions were significantly influenced by feedback frames. Some users overrode personal judgments, believing the system had access to privileged information about their emotions. Based on these findings, we explore design implications for personal informatics including risks of users trusting systems that seemingly "unlock" hidden aspects of the self. We propose design approaches that provide opportunities for future emotion-monitoring systems to exploit these framing effects, and for users to more actively construe emotional states.