Voice assistants’ inability to serve people-of-color and non-native English speakers has largely been documented as a quality-of-service harm. However, little work has investigated what downstream harms propagate from this poor service. How does poor usability materially manifest and affect users’ lives? And what interaction designs might help users recover from these effects? We identify 6 downstream harms that propagate from quality-of-service harms in voice assistants. Through interviews and design activities with 16 multicultural participants, we unveil these 6 harms, outline how multicultural users uniquely personify their voice assistant, and suggest how these harms and personifications may affect their interactions. Lastly, we employ techniques from psychology on communication repair to contribute suggestions for harm-reducing repair that may be implemented in voice technologies. Our communication repair strategies include: identity affirmations (intermittent frequency), cultural sensitivity, and blame redirection. This work shows potential for a harm-repair framework to positively influence voice interactions.

Language technologies have a racial bias, committing greater errors for Black users than for white users. However, little work has evaluated what effect these disparate error rates have on users themselves. The present study aims to understand if speech recognition errors in human-computer interactions may mirror the same effects as misunderstandings in interpersonal cross-race communication. In a controlled experiment (N=108), we randomly assigned Black and white participants to interact with a voice assistant pre-programmed to exhibit a high versus low error rate. Results revealed that Black participants in the high error rate condition, compared to Black participants in the low error rate condition, exhibited significantly higher levels of self-consciousness, lower levels of self-esteem and positive affect, and less favorable ratings of the technology. White participants did not exhibit this disparate pattern. We discuss design implications and the diverse research directions to which this initial study aims to contribute.

User adoption of security and privacy (S&P) best practices remains low, despite sustained efforts by researchers and practitioners. Social influence is a proven method for guiding user S&P behavior, though most work has focused on studying peer influence, which is only possible with a known social graph. In a study of 104 Facebook users, we instead demonstrate that crowdsourced S&P suggestions are significantly influential. We also tested how reflective writing affected participants’ S&P decisions, with and without suggestions. With reflective writing, participants were less likely to accept suggestions — both social and Facebook default suggestions. Of particular note, when reflective writing participants were shown the Facebook default suggestion, they not only rejected it but also (unknowingly) configured their settings in accordance with expert recommendations. Our work suggests that both non-personal social influence and reflective writing can positively influence users’ S&P decisions, but have negative interactions.