# Standard Language Ideology in AI-Generated Language

arXiv: https://arxiv.org/abs/2406.08726 · HTML: https://mlsanity.com/paper/2406.08726/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 103 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Investigating African-American Vernacular English in Transformer-Based Text Generation](https://mlsanity.com/paper/2010.02510/) (2020)
- [Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://mlsanity.com/paper/2303.13592/) (2023)
- [Masakhane -- Machine Translation For Africa](https://mlsanity.com/paper/2003.11529/) (2020)
- [VALUE: Understanding Dialect Disparity in NLU](https://mlsanity.com/paper/2204.03031/) (2022)
- [AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages](https://mlsanity.com/paper/2005.00085/) (2020)
- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [Evaluation of African American Language Bias in Natural Language Generation](https://mlsanity.com/paper/2305.14291/) (2023)
- [Geographic Citation Gaps in NLP Research](https://mlsanity.com/paper/2210.14424/) (2022)
- [Fairness in Language Models Beyond English: Gaps and Challenges](https://mlsanity.com/paper/2302.12578/) (2023)
- [Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics](https://mlsanity.com/paper/2012.02394/) (2020)
- [Bending the Automation Bias Curve: A Study of Human and AI-based Decision Making in National Security Contexts](https://mlsanity.com/paper/2306.16507/) (2023)

## Built on by (most cited first, 5 of 5)

- [Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks](https://mlsanity.com/paper/2410.11005/) (2024)
- [Emergence of Biased Consensus in Multi-Agent LLM Debates](https://mlsanity.com/paper/2608.02827/) (2026)
- [The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing](https://mlsanity.com/paper/2506.06761/) (2025)
- [DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English](https://mlsanity.com/paper/2601.22888/) (2026)
- [Cripping AI: Reimagining AI Through Lived Disability Experiences](https://mlsanity.com/paper/2605.02080/) (2026)

## Abstract (arXiv)

Large language models (LLMs) generate text that reinforces standard language ideology: a bias towards certain language varieties that are granted more prestige, authority, and legitimacy than others. This paper contributes a sociotechnically grounded faceted taxonomy that illustrates how generative AI systems reproduce standard language ideology and its societal implications. We introduce the concept of standard AI-generated language ideology to explain how AI systems confer legitimacy on certain language varieties while marginalizing others, structuring patterns of performance disparity, stereotyping, appropriation, and erasure. We then discuss ongoing tensions around what constitutes desirable system behavior, as well as advantages and drawbacks of generative AI tools attempting or refusing to imitate different language varieties. To address the power relations shaping generative AI and the mechanisms identified in our taxonomy--legitimation, stereotyping, appropriation, and erasure--we offer recommendations that emphasize accountability, community agency, control, and ownership. These recommendations recognize linguistic diversity as a resource to be protected, valued, and sustained as part of a just AI future.