Language variation and algorithmic bias: understanding algorithmic bias in British English automatic speech recognition

Nina Markl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

All language is characterised by variation which language users employ to construct complex social identities and express social meaning. Like other machine learning technologies, speech and language technologies (re)produce structural oppression when they perform worse for marginalised language communities. Using knowledge and theories from sociolinguistics, I explore why commercial automatic speech recognition systems and other language technologies perform significantly worse for already marginalised populations, such as second-language speakers and speakers of stigmatised varieties of English in the British Isles. Situating language technologies within the broader scholarship around algorithmic bias, consider the allocative and representational harms they can cause even (and perhaps especially) in systems which do not exhibit predictive bias, narrowly defined as differential performance between groups. This raises the question whether addressing or “fixing” this “bias” is actually always equivalent to mitigating the harms algorithmic systems can cause, in particular to marginalised communities.
Original languageEnglish
Title of host publicationProceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2022)
PublisherACM Association for Computing Machinery
Pages521-534
Number of pages14
ISBN (Electronic)978-1-4503-7252-7
DOIs
Publication statusPublished - 20 Jun 2022
Event5th Annual ACM Conference on Fairness, Accountability, and Transparency - Seoul, Korea, Republic of
Duration: 21 Jun 202224 Jun 2022
Conference number: 5
https://facctconference.org/2022/index.html

Conference

Conference5th Annual ACM Conference on Fairness, Accountability, and Transparency
Abbreviated titleFAccT 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period21/06/2224/06/22
Internet address

Keywords / Materials (for Non-textual outputs)

  • algorithmic bias
  • speech and language technologies
  • language variation
  • speech recognition

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