Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology

TL;DR

This review explores how bias and fairness are understood and addressed in speech recognition systems for African American English, emphasizing the need for governance and community involvement to ensure equitable technology.

Contribution

It provides a comprehensive overview of current research on bias in ASR for African American English and proposes a governance-centered framework for responsible development.

Findings

Growing technical fairness interventions in ASR

Critical gap in governance and community agency

Emergent interdisciplinary framework for responsible ASR

Abstract

This scoping literature review examines how fairness, bias, and equity are conceptualized and operationalized in Automatic Speech Recognition (ASR) and adjacent speech and language technologies (SLT) for African American English (AAE) speakers and other linguistically diverse communities. Drawing from 44 peer-reviewed publications across Human-Computer Interaction (HCI), Machine Learning/Natural Language Processing (ML/NLP), and Sociolinguistics, we identify four major areas of inquiry: (1) how researchers understand ASR-related harms; (2) inclusive data practices spanning collection, curation, annotation, and model training; (3) methodological and theoretical approaches to linguistic inclusion; and (4) emerging practices and design recommendations for more equitable systems. While technical fairness interventions are growing, our review highlights a critical gap in governance-centered approaches that foreground community agency, linguistic justice, and participatory accountability. We propose a governance-centered ASR lifecycle as an emergent interdisciplinary framework for responsible ASR development and offer implications for researchers, practitioners, and policymakers seeking to address language marginalization in speech AI systems.