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

2025-08-20

AI Summary

This study addresses fairness and systemic bias in automatic speech recognition (ASR) and spoken-language technologies affecting African American English (AAE) speakers. Through a scoping review of 44 studies, it identifies three interrelated structural drivers: data bias (e.g., underrepresentation, mislabeling), methodological exclusivity (e.g., monolingual, standard-English-centric evaluation), and ethical deficits in system design. The study proposes a governance-centered ASR lifecycle framework that foregrounds community agency, linguistic justice, and participatory accountability—addressing a critical gap in socio-technical oversight within current fairness interventions. Four core domains emerge as priorities: inclusive data practices, harm identification protocols, integration of sociolinguistic and technical methodologies, and actionable design guidelines. Collectively, these contributions provide an evidence-based foundation and concrete pathways for interdisciplinary collaboration to mitigate linguistic marginalization in speech AI. (149 words)

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

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.

Problem

• Addressing bias and fairness in ASR for African American English speakers
• Examining conceptual gaps in equity approaches for speech technologies
• Proposing governance frameworks for linguistically inclusive AI systems

Innovation

• Scoping review of bias and equity
• Identifies inclusive data practices
• Proposes governance-centered ASR lifecycle