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

arXiv: https://arxiv.org/abs/2508.18288 · HTML: https://mlsanity.com/paper/2508.18288/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://mlsanity.com/paper/1912.11078/) (2019)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Detoxifying Language Models Risks Marginalizing Minority Voices](https://mlsanity.com/paper/2104.06390/) (2021)
- [Investigating African-American Vernacular English in Transformer-Based Text Generation](https://mlsanity.com/paper/2010.02510/) (2020)
- [Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR](https://mlsanity.com/paper/2202.12603/) (2022)
- [Disambiguation of morpho-syntactic features of African American English -- the case of habitual be](https://mlsanity.com/paper/2204.12421/) (2022)
- [LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects](https://mlsanity.com/paper/2202.09529/) (2022)
- [Challenges in Designing Racially Inclusive Language Technologies](https://mlsanity.com/paper/2303.13546/) (2023)
- [Considerations for Ethical Speech Recognition Datasets](https://mlsanity.com/paper/2305.02081/) (2023)

## Built on by (most cited first, 2 of 2)

- [ASR Under the Stethoscope: Evaluating Biases in Clinical Speech Recognition across Indian Languages](https://mlsanity.com/paper/2512.10967/) (2025)
- [Evaluating Bias in Phoneme-Based Automatic Speech Recognition Systems: An Analysis of IPA Transcription Models](https://mlsanity.com/paper/2606.11639/) (2026)

## Abstract (arXiv)

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.