Standard Language Ideology in AI-Generated Language

In plain words

Chatbots and other language AI tend to talk in one "standard" style of English, usually American English. People who speak other varieties of the language, such as African American English or Singlish, often get worse answers from these tools, are sometimes treated as suspect, or find their speech imitated in caricature. The authors call this pattern standard AI-generated language ideology: the idea that some ways of speaking are the correct ones is baked into the tools.

The paper does not run an experiment. It collects findings from earlier studies and sorts them into five groups of problems: the standard style becomes the default, some varieties get a worse service, some are stereotyped, some are borrowed without credit, and some are pushed out of use. It then argues that fixing this is a question of who holds power over these systems, and it suggests three steps: test for language-variety harms, let speaker communities build their own data and models, and give those communities a real say in how the big systems are built.

TL;DR

• The paper is a position and taxonomy paper with no new model, training run or benchmark §3.
• It defines standard AI-generated language ideology and links LLM behaviour to the four mechanisms of legitimation, stereotyping, appropriation and erasure §2.3 §4.
• The taxonomy has five open problems, summarised in tab. 1 and discussed in §4.
• Recommendations cover language-variety evaluation, community-led data and models, and participatory governance §5.
• Use it to frame harm analysis and evaluation design. It gives no numbers to implement.

Problem

The authors ask how generative AI systems reproduce linguistic hierarchies. They say that performance gaps between language varieties are only part of the issue, and that the wider pattern is social and institutional rather than an engineering defect §1 §4. The paper does not state a quantitative target, dataset or failure rate of its own.

Method

The paper's "method" is a conceptual synthesis, not an algorithm. The paper gives no algorithm box or equation. The loop below is our restatement of the analysis procedure described in §3, not code from the paper.

# Taxonomy synthesis, as described in the paper  [§3]
# Input:  theory (four mechanisms) [§2.3]; empirical studies [§2.2]
# Output: faceted taxonomy with five facets  [tab. 1]

def build_taxonomy(mechanisms, studies):
    # 1. Start from the theoretical categories  [§3]
    facets = list(mechanisms)
    for study in studies:
        # 2. Record the harm category and dynamic the study shows  [§3]
        category = classify_harm(study)
        # 3. Refine categories when the evidence does not fit  [§3]
        facets = refine(facets, category)
    # 4. Attach documented manifestations and inferred implications  [§3]
    return facets

The steps in prose:

1. Draw on sociolinguistic theory of language ideology and identify four mechanisms that sustain standard language ideology: legitimation, stereotyping (racialisation), appropriation and commodification, and erasure §2.1 §2.3.
2. Review empirical studies of LLM and NLP behaviour on non-standard varieties, mainly around performance gaps and stereotyped outputs §2.2.
3. Map the empirical findings onto those four mechanisms. The authors describe this as an iterative process combining deductive categories from theory with inductive refinement from the studies §3.
4. Organise the result into a faceted taxonomy with five facets, each with the connection to standard language ideology, documented AI manifestations, and potential societal implications §4 tab. 1.
5. Define standard AI-generated language ideology as the combined effect of these mechanisms §4.1, and discuss why there is no single correct model behaviour for varieties §4.2.
6. Derive three recommendations at the level of power and governance §5.

No algorithm, pseudocode, equation or figure is given. The paper has no hyperparameters. The taxonomy's documented manifestations come from the cited studies, and the societal implications are the authors' inferences from theory; the paper says they are plausible trajectories, not exhaustive ones §3.

Taxonomy facets tab. 1

The five facets are the rows of tab. 1. Each has a connection to the theory, documented manifestations from cited work, and implications the authors infer.

The taxonomy is the paper's main contribution §1 §4.

Standard AI-generated language ideology §4.1

The paper treats the combined effect of the five facets as one ideology. It says the system (a) makes standard varieties, particularly SAE, the normal and appropriate way of producing language, (b) treats other varieties as deviant, risky or dispensable, (c) gives worse service to non-standard speakers, which can push them to switch to the standard variety, and (d) when it does produce non-standard varieties, tends to do so in stereotyped or appropriative ways §4.1. The paper also raises sovereign and multilingual AI programmes as a case where language hierarchy may move to a new centre rather than disappear §2.4 §4.1.

Recommendations §5

• Expand evaluation to language-variety harms §5.1. Add language variety as a category beside race and gender in evaluation frameworks; build benchmarks for erasure, stereotyping and condescension against varieties; and publish results.
• Support community-led datasets and models §5.2. Fund and support speaker communities building their own data and models, with non-extractive data practices. The paper cites Masakhane, AI4Bharat and Te Hiku Media as examples §5.2. For sovereign AI programmes it asks for community governance, support for dialect and Indigenous inclusion, data sovereignty and benefit-sharing, and explicit, accountable prioritisation of languages §5.2.
• Participatory governance and data practice for large developers §5.3. Engage minoritised communities as partners across the lifecycle; set up participatory governance; audit language datasets for representation gaps; include more diverse language in training data; and compensate and credit communities whose data is used. The paper gives the OpenAI and Icelandic government partnership as an example with an unresolved value-distribution tension §5.3.

Hyperparameters

Not applicable. The paper trains and evaluates no model, and states no hyperparameters anywhere in its source §3 §6.

Results

Not applicable as measured results. The paper reports no experiments of its own §3 §6. Its evidence is secondary: figures it quotes from cited studies. The most detailed one is from a study cited as Fleisig et al. §2.2: across eight non-standard English varieties compared with standard American and British English, model outputs showed over 84% reduction in features of the input variety, and over 96% for Jamaican, Singaporean, Scottish and African American English. Native-speaker reviews in that study rated outputs for non-standard varieties as worse on comprehension (10%), stereotyping (16%), condescension (12%) and demeaning content (22%), each relative to standard varieties §2.2. These are the cited authors' numbers, as the paper reports them, not this paper's measurements.

The paper also cites a claim that about 60% of online language content is in English, against about 17% of people speaking English globally §2.2. This is likewise quoted from a cited source.

Setup for the figures above is the cited study's, not stated in full here. Check the cited study before reusing a number.

Paper vs code

No official code. The arXiv-listed acmCodeLink in the source points to the ACM LaTeX template repository (borisveytsman/acmart), not to code by the authors, and the data link is an unfilled placeholder standard-lang-ideology.tex. A GitHub search for "standard language ideology" returned one repository, a third-party study of linguistic capital in South African schools, which is not the authors' code. A search for "AI-generated language ideology" returned no results.

Pitfalls

• Position paper, not evidence. Most claims depend on cited studies. Check the original study before citing a number, as the cited figures reflect each study's own setup.
• Implications are inference. The "societal implications" column in tab. 1 is the authors' reasoning from theory, and the paper says so §3.
• No definition of "standard AI-generated language ideology" in operational terms. It is conceptual. There is no metric or rule for detecting it in a model.
• Scope is mostly English. The authors say their analysis centres on English and SAE, and that the picture may differ in other linguistic and colonial settings §6.
• Version. The source used is arXiv v3, updated 2026-06-09. The first version date (2024-06-13) is kept as the published date. Section numbers here count \section commands in the source (1 Introduction through 7 Conclusion); the rendered PDF was not checked for numbering.
• Table reference. The text calls the taxonomy "Table 1"; the table has no number in the source, so tab. 1 follows the text.
• Sources contain an unfilled template link and placeholder data URL. Do not treat the paper as having released code or data.

Lineage

• Builds on: standard language ideology work in sociolinguistics, including the mechanisms of legitimation, stereotyping, appropriation and erasure §2.1 §2.3; empirical work on NLP and LLM performance gaps and stereotyping for non-standard varieties §2.2.
• Compared against: concurrent work surveying NLP challenges for languages and dialects is named in §1; not otherwise compared.
• Superseded by: not stated.