arXiv:2406.08726v3 Announce Type: replace Abstract: 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,
The increasing deployment and sophistication of large language models are making their embedded biases more apparent and impactful, leading to critical examination of their sociological effects.
This highlights a fundamental issue in AI development concerning fairness and equity, affecting how AI systems interact with diverse populations and potentially perpetuating social inequalities.
The understanding of AI bias expands beyond technical performance to include inherent ideological biases embedded within language models, prompting a need for more nuanced ethical and developmental approaches.
- · Ethical AI researchers
- · Linguistics and sociolinguistics experts
- · AI developers focused on bias mitigation
- · AI systems perpetuating unexamined biases
- · Users marginalized by 'standard' AI language
- · Companies ignoring ethical AI development
AI-generated text will systematically prioritize certain language varieties, leading to the marginalization of others.
This marginalization could reinforce existing social hierarchies and reduce linguistic diversity in digital spaces, particularly impacting less-resourced languages.
It might necessitate regulatory frameworks or new model architectures specifically designed to promote linguistic equity and cultural sensitivity in AI outputs globally.
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