# Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR

arXiv: https://arxiv.org/abs/2202.12603 · HTML: https://mlsanity.com/paper/2202.12603/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 86 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [AI and the Everything in the Whole Wide World Benchmark](https://mlsanity.com/paper/2111.15366/) (2021)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [What Will it Take to Fix Benchmarking in Natural Language Understanding?](https://mlsanity.com/paper/2104.02145/) (2021)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://mlsanity.com/paper/1912.11078/) (2019)
- [Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters](https://mlsanity.com/paper/2007.03001/) (2020)
- [Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research](https://mlsanity.com/paper/2112.01716/) (2021)
- [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://mlsanity.com/paper/2104.02821/) (2021)
- ["What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness](https://mlsanity.com/paper/2011.02282/) (2020)
- [The Microsoft 2017 Conversational Speech Recognition System](https://mlsanity.com/paper/1708.06073/) (2017)
- [Targeting the Benchmark: On Methodology in Current Natural Language Processing Research](https://mlsanity.com/paper/2007.04792/) (2020)
- [Bringing the People Back In: Contesting Benchmark Machine Learning Datasets](https://mlsanity.com/paper/2007.07399/) (2020)
- [Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies](https://mlsanity.com/paper/2012.09995/) (2020)
- [WER we are and WER we think we are](https://mlsanity.com/paper/2010.03432/) (2020)
- [Black-box Adaptation of ASR for Accented Speech](https://mlsanity.com/paper/2006.13519/) (2020)
- [Language Technology Programme for Icelandic 2019-2023](https://mlsanity.com/paper/2003.09244/) (2020)
- [Automatic Speech Recognition Services: Deaf and Hard-of-Hearing Usability](https://mlsanity.com/paper/1909.02853/) (2019)

## Built on by (most cited first, 7 of 7)

- [A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems](https://mlsanity.com/paper/2204.01397/) (2022)
- [Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning](https://mlsanity.com/paper/2506.17525/) (2025)
- [Improving Speech Recognition for African American English With Audio Classification](https://mlsanity.com/paper/2309.09996/) (2023)
- [Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology](https://mlsanity.com/paper/2508.18288/) (2025)
- [Goodness of Pronunciation Pipelines for OOV Problem](https://mlsanity.com/paper/2209.03787/) (2022)
- [BERnaT: Basque Encoders for Representing Natural Textual Diversity](https://mlsanity.com/paper/2512.03903/) (2025)
- [Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity](https://mlsanity.com/paper/2601.18641/) (2026)

## Abstract (arXiv)

Despite the fact that variation is a fundamental characteristic of natural language, automatic speech recognition systems perform systematically worse on non-standardised and marginalised language varieties. In this paper we use the lens of language policy to analyse how current practices in training and testing ASR systems in industry lead to the data bias giving rise to these systematic error differences. We believe that this is a useful perspective for speech and language technology practitioners to understand the origins and harms of algorithmic bias, and how they can mitigate it. We also propose a re-framing of language resources as (public) infrastructure which should not solely be designed for markets, but for, and with meaningful cooperation of, speech communities.