Bias in Automatic Speech Recognition: The Case of African American Language
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| Title: | Bias in Automatic Speech Recognition: The Case of African American Language |
|---|---|
| Language: | English |
| Authors: | Martin, Joshua L. (ORCID |
| Source: | Applied Linguistics. Aug 2023 44(4):613-630. |
| Availability: | Oxford University Press. Great Clarendon Street, Oxford, OX2 6DP, UK. Tel: +44-1865-353907; Fax: +44-1865-353485; e-mail: jnls.cust.serv@oxfordjournals.org; Web site: http://applij.oxfordjournals.org/ |
| Peer Reviewed: | Y |
| Page Count: | 18 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Automation, Speech Communication, Black Dialects, Racism, Artificial Intelligence, Racial Discrimination, Linguistics |
| DOI: | 10.1093/applin/amac066 |
| ISSN: | 0142-6001 1477-450X |
| Abstract: | Research on bias in artificial intelligence has grown exponentially in recent years, especially around racial bias. Many modern technologies which impact people's lives have been shown to have significant racial biases, including automatic speech recognition (ASR) systems. Emerging studies have found that widely-used ASR systems function much more poorly on the speech of Black people. Yet, this work is limited because it lacks a deeper consideration of the sociolinguistic literature on African American Language (AAL). In this paper, then, we seek to integrate AAL research into these endeavors to analyze ways in which ASRs might be biased against the linguistic features of AAL and how the use of biased ASRs could prove harmful to speakers of AAL. Specifically, we (1) provide an overview of the ways in which AAL has been discriminated against in the workforce and healthcare in the past, and (2) explore how introducing biased ASRs in these areas could perpetuate or even deepen linguistic discrimination. We conclude with a number of questions for reflection and future work, offering this document as a resource for cross-disciplinary collaboration. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1388779 |
| Database: | ERIC |
| Abstract: | Research on bias in artificial intelligence has grown exponentially in recent years, especially around racial bias. Many modern technologies which impact people's lives have been shown to have significant racial biases, including automatic speech recognition (ASR) systems. Emerging studies have found that widely-used ASR systems function much more poorly on the speech of Black people. Yet, this work is limited because it lacks a deeper consideration of the sociolinguistic literature on African American Language (AAL). In this paper, then, we seek to integrate AAL research into these endeavors to analyze ways in which ASRs might be biased against the linguistic features of AAL and how the use of biased ASRs could prove harmful to speakers of AAL. Specifically, we (1) provide an overview of the ways in which AAL has been discriminated against in the workforce and healthcare in the past, and (2) explore how introducing biased ASRs in these areas could perpetuate or even deepen linguistic discrimination. We conclude with a number of questions for reflection and future work, offering this document as a resource for cross-disciplinary collaboration. |
|---|---|
| ISSN: | 0142-6001 1477-450X |
| DOI: | 10.1093/applin/amac066 |
