"This Wasn't Made for Me": Recentering User Experience and Emotional Impact in the Evaluation of ASR Bias

TL;DR

This study explores how ASR bias affects users' emotional well-being, revealing that system failures cause frustration and emotional labor, which are often overlooked in traditional accuracy-focused evaluations.

Contribution

It highlights the emotional and cultural impacts of ASR bias through user experience studies, emphasizing the need to consider human-centered dimensions beyond error rates.

Findings

Participants experience frustration and emotional labor due to ASR failures.

Users perform extensive invisible labor like code-switching and hyper-articulation.

Failures lead to feelings of inadequacy despite awareness of systemic bias.

Abstract

Studies on bias in Automatic Speech Recognition (ASR) tend to focus on reporting error rates for speakers of underrepresented dialects, yet less research examines the human side of system bias: how do system failures shape users' lived experiences, how do users feel about and react to them, and what emotional toll do these repeated failures exact? We conducted user experience studies across four U.S. locations (Atlanta, Gulf Coast, Miami Beach, and Tucson) representing distinct English dialect communities. Our findings reveal that most participants report technologies fail to consider their cultural backgrounds and require constant adjustment to achieve basic functionality. Despite these experiences, participants maintain high expectations for ASR performance and express strong willingness to contribute to model improvement. Qualitative analysis of open-ended narratives exposes the deeper costs of these failures. Participants report frustration, annoyance, and feelings of inadequacy, yet the emotional impact extends beyond momentary reactions. Participants recognize that systems were not designed for them, yet often internalize failures as personal inadequacy despite this critical awareness. They perform extensive invisible labor, including code-switching, hyper-articulation, and emotional management, to make failing systems functional. Meanwhile, their linguistic and cultural knowledge remains unrecognized by technologies that encode particular varieties as standard while rendering others marginal. These findings demonstrate that algorithmic fairness assessments based on accuracy metrics alone miss critical dimensions of harm: the emotional labor of managing repeated technological rejection, the cognitive burden of constant self-monitoring, and the psychological toll of feeling inadequate in one's native language variety.