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

2026-04-22

AI Summary

This study addresses a critical gap in the evaluation of automatic speech recognition (ASR) systems by moving beyond conventional error-rate metrics to examine the emotional burden and adaptive costs users incur when ASR fails. Through qualitative research involving field interviews and open-ended narrative analysis across four U.S. dialect communities, the work integrates sociolinguistic and human-computer interaction theories to systematically investigate how ASR failures affect users’ emotions, behaviors, and self-perception. Findings reveal widespread experiences of frustration and self-doubt, alongside active linguistic accommodation strategies, underscoring significant deficiencies in cultural alignment and affective fairness. This research pioneers an expanded fairness framework for ASR that incorporates emotional impact, cultural inclusivity, and subjective user experience, thereby challenging the dominant paradigm that prioritizes accuracy alone.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• ASR bias
• user experience
• emotional impact
• algorithmic fairness
• dialect marginalization

Innovation

• emotional impact
• user experience
• algorithmic fairness
• invisible labor
• dialect bias