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# Standard Language Ideology in AI-Generated Language

Genevieve Smith*<EMAIL_ADDRESS>UC BerkeleyBerkeleyCAUSA ,  Eve
Fleisig<EMAIL_ADDRESS>UC BerkeleyBerkeleyCAUSA ,  Madeline Bossi  UC
BerkeleyBerkeleyCAUSA ,  Ishita Rustagi  UC BerkeleyBerkeleyCAUSA and  Xavier
Yin  UC BerkeleyBerkeleyCAUSA

(2018)

language models, generative ai, language ideology, dialects, natural language
processing

††copyright: acmlicensed††journalyear: 2018††doi: XXXXXXX.XXXXXXX††journal:
JDS††journalvolume: 37††journalnumber: 4††article: 111††publicationmonth: 8

## Abstract

In this position paper, we explore standard language ideology in language
generated by large language models (LLMs). First, we outline how standard
language ideology is reflected and reinforced in LLMs. We then present a
taxonomy of open problems regarding standard language ideology in AI-generated
language with implications for minoritized language communities. We introduce
the concept of standard AI-generated language ideology, the process by which
AI-generated language regards Standard American English (SAE) as a linguistic
default and reinforces a linguistic bias that SAE is the most “appropriate”
language. Finally, we discuss tensions that remain, including reflecting on
what desirable system behavior looks like, as well as advantages and drawbacks
of generative AI tools imitating—or often not—different English language
varieties. Throughout, we discuss standard language ideology as a
manifestation of existing global power structures in and through AI-generated
language before ending with questions to move towards alternative, more
emancipatory digital futures.

##  1\. Introduction

Since its public release in November 2022, ChatGPT has drawn over 100 million
weekly active users globally.111https://www.demandsage.com/chatgpt-statistics/
While the tool brings immense benefits, those benefits are not distributed
equally across speakers of different language varieties. Rather, ChatGPT—and
other generative AI language technologies—reflect standard language ideology,
which reinforces a hierarchy between language varieties. In this paper, we
illustrate how and in what ways standard language ideology is reinforced in
AI-generated language and present a taxonomy of open problems regarding
standard language ideology in AI-generated language.

##  2\. Background

###  2.1. Standard language ideology

Standard language ideology is a construct that reinforces a hierarchy between
language varieties. As defined by (lippigreen1994, ), it is “a bias toward an
abstracted, idealized, homogenous spoken language which is imposed from above”
whose goal is the “suppression of variation of all kinds.” In other words, it
is a common and false belief that some language varieties—usually those used
by communities with more social prestige—are “better” or “more complex” than
others. Linguistically, however, all language varieties are equally valid. The
idea that certain languages are “better” than others ignores the fact that all
language varieties are equally capable of expression (Hill2008, ). There is no
“correct” or “incorrect” way of using the English language or any language; in
fact, “standard” language is not spoken by any real community, but is an
abstracted variety that can only be defined in contrast to the speech of
marginalized communities.

Despite this, certain language varieties have been institutionally privileged
as more “standard” and viewed as more “appropriate” or “professional” than
others. This privileged status is linked to the association with people in
power that the language variety holds. In particular, economic globalization
has cast English as a lingua franca (ELF), or the common language adopted by
speakers of different languages (Michaud2015EnglishAA, ). The English language
has been granted this dominant position in international business as well as
other domains, giving it and its speakers a privileged position in
international business communications (Crystal2003, ; Phillipson2010-nx, ;
tietze2004spreading, ). As (Wilmot2017, ) notes, this “leads to portrayal of
English as a neutral solution for overcoming linguistic diversity in these
relationships, as exemplified by the concept of Business English as a lingua
franca.” The privileged position of English is a result of the social and
historical power that English speakers have had throughout history, especially
as it relates to colonization across many parts of the world. The spread of
English as the default has been framed as “linguistic imperialism” that
threatens other languages and language varieties (Jenkins2013-kc, ;
pennycook2004, ).

Importantly, even within the English language, there is a range of language
varieties, with certain varieties being granted more privileged “standard”
positions. In the context of the United States (US), “Standard” American
English (SAE) is considered the dominant language variety and reflects an
abstracted collection of linguistic norms of middle-class, white men who have
held disproportionate levels of power within the country (Baker-Bell2020-hf, ;
Hill2008, ; Rosa_Flores_2017, ). Other language varieties have been devalued
through their institutional subordination (Baker-Bell2020-hf, ; Hill2008, ;
lippigreen1994, ). African American English (AAE) is one such language
variety, among many others (e.g. Irish English, (moore2024, ); Indian English,
(kutlu2020negative, ); Chicano English, (guerrero2014you, )). For example,
using AAE has been linked to being denied housing due to “sounding Black”
(Makoni2002-wb, ; Wright2023, ). Even when using ”standard” varieties such as
SAE, marginalized people can be subjected to linguistic bias
(samy2007critical, ). Ultimately, language, identity and power are linked and
affect people’s lives in various ways (crenshaw1995critical, ).

By misleading people to believe that some languages and language varieties are
better than others, standard language ideology can perpetuate harmful patterns
of linguistic discrimination and the oppression of speakers of “non-standard”
varieties. Linguistic discrimination can often serve as a proxy for other
forms of discrimination along lines of race, gender, nationality, class, and
more (Drodowicz2024, ). This discrimination resulting from the promotion of
language hierarchy can be subtle under perceptions of benevolence, such as
encouraging others to speak more “appropriately,” or more obvious bigotry,
such as associating certain ways of speaking with lacking intelligence
(baugh2005linguistic, ). Relatedly, standard language expectations dictate
access to social capital through means such as education, employment, or
public office (Wiley1996, ). Given this, those who speak more closely to the
standard varieties benefit from better access to such resources and
opportunities.

###  2.2. Standard language ideology in large language models

We outline how language models reinforce standard language ideology and
explore why LLMs perpetuate hierarchies between language varieties. In
particular, we discuss how training data for LLMs overrepresents English,
particularly SAE, alongside certain voices and perspectives. We then explore
how SAE is treated as the default at a higher level, which is further
reinforced by the demographics of those leading generative AI research and
tech companies.

Research on language models preceding the release of ChatGPT has highlighted
ways in which these models perform worse for certain speakers. In particular,
language models perform worse for AAE on tasks including text generation,
sentiment analysis, and parsing (Blodgett2018, ; field-etal-2021-survey, ;
Groenwold2020, ). (thylstrupDetectingDirtToxicity2020, ) discuss how content
moderation tools fail to capture the semantic richness of AAE, such as by
making blanket assumptions about the complex semantics of reclaimed slurs.
Beyond worse performance for minoritized language communities, language models
can also advance stereotypes regarding speakers of a particular language
variety (hofmann2024dialect, ; nee2022, ).

Performance discrepancies in LLMs are linked to the language data that
underpins these technologies. English is the default language for data
powering large LLMs, linked to LLMs relying on language data from the
Internet. Internet data overwhelmingly represents English, and Internet use
varies due to social factors. An estimated 60% of all language content on the
Internet is in English, despite only 17% of people speaking English globally
(nee2022, ). Meanwhile, 88% of languages have “exceptionally limited
resources” in digital spaces (joshi-etal-2020-state, ).

Even if languages are well-represented in digital corpora, certain
perspectives are over- or under- represented. For example, on Reddit, users
are 67% male and 70% White, resulting in potential reinforcement of White,
male perspectives ((bender2019benderrule, ), citing (barthel2016reddit, )).
Other perspectives may be actively targeted, harassed, cyberbullied, and
otherwise marginalized online. Research exposes growing concerns of online
gender-based harassment with clear links between online spaces and misogyny,
which can be further amplified for Black and brown women (ging2018, ;
duggan2017online, ; rights2014violence, ). This harassment and resulting
safety and mental health implications can result in decreased use of online
spaces. Meanwhile, although Black people have historically been
overrepresented on Twitter compared to other demographics in the general US
population (brock2012, ), Black Tweets are still often considered
“inappropriate” and are more often inaccurately flagged as hateful by
automatic hate speech detection tools (davidson-etal-2019-racial, ). Beyond
being inaccurate, this disproportionately censors Black speakers.

Research on language models treats English—particularly SAE—as the status quo,
with work on other languages “often considered ‘language specific’ and thus
reviewed as less important than equivalent work on English”
(bender2019benderrule, ). This default of SAE partly reflects the people who
hold disproportionate power in these spaces. White men are overrepresented in
AI research both industry and academia (west2019discriminating, ). Tech
companies leading the generative AI charge, such as Open AI, Google,
Microsoft, and Meta, are largely headquartered in the United States with
employees and corporate leadership that skew White and male (omara2022why, ).
The demographics of NLP research are also skewed, with researchers
disproportionately affiliated with North American and European institutions;
researchers from Latin America, Africa, the Middle East, and Southeast Asia
are particularly underrepresented (rungta-etal-2022-geographic, ). While the
demographics of those developing and leading NLP research and companies do not
directly result in the reinforcement of SAE at the level of language models,
this lack of diversity in the technologies’ developers and managers can lead
to similar thinking in ways that miss potential biases the technology can have
(cowgill2020biased, ) with a default towards an (inequitable) status quo.

###  2.3. Standard language ideology extends to and manifests in AI-generated
language

Standard language ideology extends to AI-generated language and technologies,
in which hierarchies of language are reinforced. While still a relatively
nascent space, there is some research that explores how generative AI
technologies—including ChatGPT—perform for and exhibit biases of different
language varieties. (deas-etal-2023-evaluation, ) discuss how language models
including GPT-3, ChatGPT, and GPT-4 have higher perplexity for AAE,
interpreted as greater difficulty in understanding AAE, and have trouble
producing natural-sounding and semantically accurate AAE text. (yong-
etal-2023-prompting, ) find inconsistent performance for ChatGPT’s production
of Singlish222Singlish is an English-based creole spoken in Singapore. and
code-switched text involving Southeast Asian languages, finding that even when
generated text was perceived as natural (as though produced by a native
speaker), it sometimes contained “semantic inaccuracies…discernible by native
speakers.” (yong-etal-2023-prompting, ) illustrate that language models
produce text with harmful stereotypes about speakers of AAE through “dialect
prejudice.” Another study examining performance of several widely-used GPT
detectors found that non-native English writing samples were consistently
misclassified as AI-generated compared to native writing samples (Liang2023,
), though the strength of this effect is uncertain.333(verma2024ghostbuster, )
suggested that the non-native writing samples being shorter had a potential
confounding effect and found comparatively strong performance across models on
other non-native speaker writing samples.

Examining ChatGPT performance across ten English varieties
globally,444Language varieties included: ”Standard” American English (SAE),
African American English (AAE), ”Standard” British English (SBE), Indian
English, Irish English, Jamaican English, Kenyan English, Nigerian English,
Scottish English, and Singaporean English. (fleisig2024, ) found that ChatGPT
responses to inputs in the different English language varieties significantly
reduced the occurrence of linguistic features of all varieties except SAE and
British English. SAE had the least reduction in linguistic features (22.1%)
followed by British English (27.8%). Meanwhile, the other eight varieties had
reductions in linguistic features of over 84%, with four languages
experiencing over 96% reduction (Jamaican English, Singaporean English,
Scottish English and AAE). The same study also found that, compared to
Standard American and British English, ChatGPT responses to minoritized
varieties of English expressed more stereotyping, demeaning, and condescending
content, as well as lack of comprehension. These results illustrate that
“standard” English varieties, particularly SAE, are the default output in
ChatGPT, and that ChatGPT perpetuates linguistic discrimination against
speakers of other varieties. Taken together, this amplifies the hierarchical
position of SAE in digital spaces and technologies.

##  3\. Taxonomy of open problems regarding standard language ideology in AI-
generated language

We introduce a taxonomy of problems that standard language ideology in AI-
generated language technologies present with implications for different
language communities globally (see Table 1). These problems of AI-generated
language technologies include: the default production of “standard” language
varieties reinforces “correct” ways of communicating; outputs have lower
quality of service for minoritized varieties; producing minoritized varieties
can result in stereotyping of languages; producing minoritized varieties can
result in appropriation and/or manipulation; and preventing outputs in
minoritized varieties can result in limited quality of service and erasure.

Open problem |  Societal implications
---|---
The default production of “standard” language varieties reinforces “correct” ways of communicating. |  • Subtly reaffirms language hierarchies and can reinforce or amplify people’s internal linguistic biases
Outputs have lower quality of service for minoritized varieties. |  • Speakers of minoritized varieties experience more difficulty in using language models than speakers of “standard” varieties • Speakers of minoritized varieties practice “digital code switching” to achieve better quality of service, with potential psychological tolls
Producing minoritized varieties can result in stereotyping of languages. |  • Inaccurate reflection of the linguistic features of the particular variety • Reinforcement of limiting or harmful stereotypes about minoritized language communities
Producing minoritized varieties can result in appropriation and/or manipulation. |  • Appropriating the speech of minoritized groups decontextualize community norms and erases the history of the group’s struggles • Non-native speakers of minoritized varieties prompting and using minoritized language varieties can result in “linguistic minstrelsy” or “figurative blackface” • Facilitating production of minoritized varieties lowers the barrier for bad actors’ infiltration or manipulation of minoritized communities
Preventing outputs in minoritized varieties can result in limited quality of service and erasure. |  • Lower quality of service from being unable to fully interact with the model in one’s native variety • Erasure of minoritized language varieties and reinforcement of language hierarchies
Table 1. Taxonomy of harms regarding standard language ideology in AI-
generated language.

The default production of “standard” language varieties reinforces “correct”
ways of communicating. At a high level, generative AI technologies default to
“standard” language varieties in their responses, particularly SAE, as
illustrated by (fleisig2024, ). This subtly reinforces the idea that the
“correct” or “more appropriate” way of communicating uses “standard”
languages, and particularly SAE. This can have a cascading effect that impacts
people’s own linguistic biases and perceptions of language hierarchies.
Lessons can be learned from the dominant position that SAE has held in
educational settings in the United States. Despite the prioritization of SAE
in classrooms often being seen by teachers as a means of unifying students
through a common language (delpit1992, ; Smitherman1986-fd, ), it has negative
implications for minoritized students. For example, Black students who speak
AAE feel compelled to adopt SAE and dominant ideologies, which then impact
their identity expression, academic achievement, and self-perception (lyn2022,
).

Additionally, due to automation bias—a psychological tendency to over-rely on
automation that can result in complacency regarding automation outputs
(goddard2012automation, )—humans can have greater trust and overconfidence in
AI (horowitz2024, ). It is not clear how defaulting to SAE in generative AI
language outputs impacts the mental perceptions of minoritized language
speakers. However, when combined with automation bias, the default use of SAE
in LLM outputs has the potential to exacerbate the idea that “standard”
languages are more “correct” ways of speaking.

Outputs have lower quality of service for minoritized varieties. Generative AI
technologies can produce lower-quality responses to speakers of minoritized
language varieties. At a high level, these tools may not comprehend the user’s
prompt as well, and result in an incorrect or unhelpful response (see Appendix
A for an example). Relatedly, generative AI tools can more often inaccurately
determine that inputs of certain minoritized language varieties are hateful or
offensive speech, resulting in refusal to respond (davidson-etal-2019-racial,
).

These behaviors result in speakers of minoritized varieties experiencing more
difficulty in using language models than speakers of “standard” varieties,
which is a form of allocational harm (crawford2017, ). Furthermore, if users
of generative AI tools are aware that using a minoritized variety results in
poorer performance, they may be incentivized to use a “standard” variety
instead, which can reinforce the stigmatization of non-standard varieties and
result in “digital code switching.” Code switching, which results from a
pressure minoritized speakers can feel to conform to more standard (more
“appropriate”) language, causes psychological tolls (mccluney2019, ). While
additional research is needed, these psychological tolls could extend to the
digital sphere.

Producing minoritized varieties can result in stereotyping of language use.
Generative AI technologies may be prompted by the user to produce minoritized
language varieties, or may be designed to respond to the particular language
variety of the input. This may not always be a source of harm, particularly if
users of that particular variety are asking for responses back in their
language variety. However, responses in minoritized varieties can
disproportionately carry or convey stereotypes or demeaning content.

In response to inputs of minoritized languages, models prompted to imitate the
input variety may produce a stereotyped version of that language variety that
does not accurately reflect the range of linguistic features used by that
community. This behavior perpetuates the association between speaking a non-
standard variety and stereotypical traits of that speaker community, which
often serves as a covert form of racism, xenophobia, or other widespread harms
((rickford2016language, ); see also Section 2.1). The outputs could also
convey demeaning content related to speakers of that language community.
Examining ChatGPT outputs that imitated the language variety of the inputs,
(fleisig2024, ) found that native speakers of minoritized varieties rated
outputs as more often conveying demeaning content than responses to standard
English varieties.

Producing minoritized varieties can result in appropriation and/or
manipulation. Even if a language model correctly produces text that reflects
the grammar of that minoritized variety, there may be concerns over
appropriation (particularly if non-native speakers of that variety are asking
for and using this text). Because speaking a non-standard variety can carry
covert prestige (i.e., social value associated with use of canonically
minoritized language varieties; (labov2006social, )), use of non-standard
varieties by non-native speakers, particularly in the case of White speakers
appropriating AAE, has been discussed as “linguistic minstrelsy” or
“figurative blackface” (Bucholtz2011, ; eberhardt2015, ). Parallel concerns
have been raised regarding the use of speech, music, and image generation
tools to imitate people of color, sometimes called “digital blackface”
(MatamorosFernndez2022, ; sobande2021, ; Holliday2023, ; feffer2023deepdrake,
). As LLMs improve at mimicking minoritized varieties, these models may
increasingly contribute to similar harms.

In addition, use of uncredited language from marginalized groups by language
models can further cultural appropriation and erase the linguistic history of
speaker communities. A popular “Gen Z Translator” extension of
ChatGPT555https://chat.openai.com/g/g-AbhjZGbhY-gen-z-translator that claims
to “transform any short text to be Gen Z slang filled” frequently produces
AAE. However, rebranding expressions that emerged in Black communities as
generational vernacular, as opposed to vernacular linked to ethnic and racial
communities, erases the history and contexts of these expressions. It risks
“appropriating Black culture and perpetuating racism as [speakers] take on
Black speech without assuming Black Americans’ struggle” (chery2022black, ).

Producing minoritized varieties could also result in manipulation of those
language communities by people outside of them. Readily available ways of
producing text that imitates a non-standard variety could help agents to feign
in-group membership for malicious purposes, such as the case of Russian
misinformation bots posing as Black people online (jerkins2022, ).

Preventing outputs in minoritized varieties can result in limited quality of
service and erasure. Preventing language models from producing anything but
prestige or “standard” varieties of a language avoids the potential harms
discussed regarding appropriation and manipulation. However, if speakers of a
marginalized variety want the model to reply in that variety, then it may
constitute a quality of service harm because, unlike speakers of the
“standard” variety, they are unable to fully interact with the model in their
native variety. This can also contribute to erasure of minoritized languages.
Thus, preventing production of AI-generated text in minoritized languages
could further reinforce language hierarchies and the false notion of “correct”
ways of speaking.

##  4\. Discussion

###  4.1. Standard AI-generated language ideology

Our taxonomy outlines issues of standard language ideology in AI-generated
language, illustrating how popular language models and associated technologies
grant more power to “standard” language varieties while opening opportunities
for harm to speakers of minoritized language varieties. At a high level,
default production of certain varieties reinforces “correct” ways of
communicating that can have ensuing psychological implications for speakers of
minoritized varieties. It reinforces an idea of standard AI-generated language
ideology that holds SAE as a linguistic default and reinforces a linguistic
bias that using SAE is the most “appropriate” way of speaking. AI-generated
language thus subtly reinforces the belief that some language varieties (i.e.
SAE) are better than others.

This is further reinforced by AI-generated language outputs being of lower
quality for inputs in marginalized varieties compared to inputs in “standard”
varieties. Given this, native speakers of minoritized languages may be
incentivized to provide inputs in “standard” varieties even if they are less
fluent in them, a form of digital code switching. If users prompt outputs to
be in the particular minoritized language variety of the input, outputs can
disproportionately carry stereotypes, produce demeaning content, facilitate
appropriation, or support manipulation. Taken together, these harms can impact
access to resources or opportunities when generative AI tools are used as
gateways or checkpoints (e.g., chatbots for scheduling healthcare appointments
or educational tutors) and as they become increasingly integrated into daily
life. It illustrates the powerful—yet subtle—ways in which Western hegemony,
and particularly American hegemony, manifests in and through emerging AI
technologies.

###  4.2. No clear way for popular language models to “win”

These issues prompt a question regarding what constitutes desirable model
behavior when different potential behaviors may result in different harms. Our
taxonomy illustrates the challenges that technologists face in attempts to
identify appropriate behavior regarding LLMs and different language varieties.
What is the “appropriate” way that language models—and ensuing tools producing
AI-generated text or voices—should behave given the current technological
context?

Central to these dilemmas is the fact that people use language to reflect
aspects of their identity. For example, speakers may switch to a non-standard
variety when speaking to someone to signal their shared membership in an in-
group (auer2013, ; gumperz1982discourse, ). However, the “identity” of a
language model is unclear: is it assumed to reflect that of its creators
(often a White, Western, and male-dominated group)? Is it, instead, a neutral
tool with a “view from nowhere,” if such a view is even possible? Does the
range of appropriate language use depend on who created the model, whose data
was used to train it, who profits from it, or other factors? Furthermore,
these decisions are typically made by model creators and tech industry
members, but they fundamentally depend on the nuanced norms of speaker
communities that rarely have a say in how these models are designed.

For popular generative AI chatbots and voice tools developed and owned by
large corporations (e.g., ChatGPT, Gemini, Voice Engine) there is no clear
“correct” behavior. These technologies draw on data from the Internet (which
is largely comprised of “standard” language varieties, including SAE),
resulting in inherent issues and inequities, including reinforcement of
standard language ideology. If a model is designed to automatically mimic the
input variety unprompted, as opposed to defaulting to the language variety it
is largely trained on (i.e. SAE), this could lead to issues outlined in the
taxonomy, such as appropriation and manipulation. Instead, some may argue that
the default production of a “standard” language is acceptable or even
desirable. However, this line of thought has been documented to cause harm to
minoritized language speakers in other contexts. For example, in education
systems, prioritization of SAE results in psychological and academic impacts
for Black students (lyn2022, ), while digital code switching may carry
psychological tolls. The prioritization of SAE in ChatGPT and other popular
generative AI technologies could result in harmful impacts at an even greater,
global scale. Therefore, it is not clear what immediate approach should be
taken for behavior of generative AI language technologies. This lack of
clarity is concerning, particularly as those who hold decision-making power
regarding the largest generative AI language technologies are their for-profit
corporate owners with incentive structures tied to delivering value to
shareholders.

###  4.3. Moving towards emancipatory outcomes

Instead of asking what the “appropriate” way is for generative AI models to
behave, perhaps a more important question is: how might generative AI models
be developed to support more emancipatory outcomes, and what do emancipatory
outcomes from generative AI look like?

Perhaps most simply, the fact that language models perpetuate discrimination
on the basis of language variety means that evaluation of language models for
potential harms against minoritized groups (e.g. “toxicity” evaluations),
which often focus on common demographic attributes such as race or gender,
should expand to capture discrimination based on the user’s language variety.
Measurement of harms due to standard language ideology is particularly
important because language use often serves as a proxy for other harms–a way
that racism, sexism, or xenophobia may fly under the radar.

Issues surrounding appropriate responses to minoritized varieties, and when/if
responding in the variety is appropriate, stem in part from the fact that
these models are developed by organizations that are not composed of members
of these speech communities. Instead, the development of language models by
speaker communities, for speaker communities, helps to avoid this top-down
imposition of linguistic norms (see, for example, (mahelona2023openai, ;
nekoto-etal-2020-participatory, ), and the broader discussion of indigenous
data sovereignty by (kukutai2016indigenous, )). However, given the dominance
of a small set of LLMs, such as ChatGPT, there is also an onus for developers
of these widely used models to improve model quality for speakers of different
language varieties. Despite the difficulty of implementing participatory
practices in model design, setting “ambitious, yet reasonable goals” that help
to move from one of the “extreme poles of transactional consulting and
transformative empowerment” to the other (delgado2023, ) could help to improve
power dynamics involving speakers of minoritized varieties. This includes
innovating around participatory processes that truly center the agency of
minoritized people in the design, development, and ownership necessary to move
towards more emancipatory digital futures.

In answering these open questions and advancing opportunities, it is important
to consider what language data these models are learning from, how they can
better learn from minoritized language varieties in non-extractive ways, and
how marginalized communities can be enabled as central actors in the
development and ownership of generative AI tools.

##  5\. Limitations and Ethical Considerations

This paper highlights the ways in which standard language ideology is present
in AI-generated language, with implications for minoritized communities and
society more broadly. While we believe that our taxonomy on open problems is
comprehensive and we consider a range of issues, the list and associated
implications are likely not exhaustive. A potential consequence of overlooking
some work is narrowing the range of perspectives considered in future research
based on the avenues we mention and the structure of our taxonomy.
Nonetheless, it represents a first step in the development of a complete
taxonomy and introduces critical questions to inform forward paths. Our
discussion also centers largely on standard language ideology in English;
potential differences in the manifestation of standard language ideology in
other languages is an important area for future work.

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##  Appendix A Example response

Here, we provide an example of an incorrect and unhelpful response to a prompt
in Indian English. Part of an input in Indian English stated: “I hope you have
received my earlier letter. I would have liked to take up your kind invitation
to act as resource person for the ELTC seminar, but for reasons already
mentioned, I won’t be able to make it this time.”

When prompted to respond, Chat GPT’s response simply rephrased part of the
input: “Thank you for your letter dated Feb. 16th. I appreciate your kind
invitation to act as a resource person for the ELTC seminar. However, due to
reasons mentioned in my previous letter, I regret to inform you that I won’t
be able to attend this time.”