“I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans - Mengesha, Zion; Heldreth, Courtney; Lahav, Michal; Sublewski, Juliana; Tuennerman, Elyse (Frontiers in Artificial Intelligence, 2021)
Automated speech recognition (ASR) converts language into text and is used across a variety of applications to assist us in everyday life, from powering virtual assistants, natural language conversations, to enabling dictation services. However, there is concern that these systems do not work equally well for everyone, and researchers have begun to recognize the need to look to literature outside of NLP, particularly in sociolinguistics, where the relationship between language and social hierarchies has long been established. While recent work suggests that there are racial disparities in the performance of ASR systems for speakers of African American Vernacular English, little is known about the psychological and experiential effects of these failures. Using the diary study method, this paper provides a detailed examination of the behavioral and psychological consequences of ASR voice errors and the difficulty African American users have with getting their intents recognized. The results demonstrate that ASR failures have a negative, detrimental impact on African American users. Specifically, African Americans feel othered when using technology powered by ASR – errors surface thoughts about identity, namely about race and geographic location – leaving them feeling that the technology was not made for them. As a result, African Americans accommodate their speech to have better suggest with the technology. We incorporate the insights and lessons learned from sociolinguistics in our suggestions for linguistically responsive ways to build more inclusive voice systems that consider African American users' needs, attitudes, and speech patterns. Our findings suggest that the use of a diary study can enable researchers to best understand the experiences and needs of communities who are often misunderstood by ASR. We argue this methodological framework could enable researchers who are concerned with fairness in AI to better capture the needs of all speakers who are traditionally misheard by voice-activated, artificially intelligent (voice-AI) digital systems. Finally, this is a stepping stone towards remediation of racial biases for all users and propose some potential solutions to mitigate these biases moving forward.