Cripping AI is a proposed framework that dismantles ableist assumptions in AI by centering disabled ways of knowing and respecting disabled labor in co-creation.
InProceedings of the 22nd Workshop on Treebanks and Lin- guistic Theories (TLT 2024), pages 30–39, Ham- burg,Germany
3 Pith papers cite this work. Polarity classification is still indexing.
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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, 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.
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LLMs show practical but imperfect ability to replicate human annotations of language ideologies in Luxembourgish comments, with some gains from machine translation to high-resource languages.
Ctx2Skill uses a self-evolving multi-agent loop with Challenger, Reasoner, Judge, and Cross-time Replay to discover context-specific skills, improving task-solving rates on CL-bench benchmarks across models.
citing papers explorer
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Cripping AI: Reimagining AI Through Lived Disability Experiences
Cripping AI is a proposed framework that dismantles ableist assumptions in AI by centering disabled ways of knowing and respecting disabled labor in co-creation.
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Language Ideologies in a Multilingual Society: An LLM-based Analysis of Luxembourgish News Comments
LLMs show practical but imperfect ability to replicate human annotations of language ideologies in Luxembourgish comments, with some gains from machine translation to high-resource languages.
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From Context to Skills: Can Language Models Learn from Context Skillfully?
Ctx2Skill uses a self-evolving multi-agent loop with Challenger, Reasoner, Judge, and Cross-time Replay to discover context-specific skills, improving task-solving rates on CL-bench benchmarks across models.