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• Large Language Models (LLMs) and generative AI are 'unfair' to less-spoken languages and deepen the digital language divide.limitation 95%
• LLMs and GenAI technologies are made possible by and exacerbate historical processes of linguistic standardization rooted in European nationalist and colonial projects.limitation 90%
• South Tyrolean dialects, despite being the primary medium of everyday interaction in South Tyrol, Italy, remain largely absent from LLM training data.limitation 85%
• The orthographic diversity of Kurdish (Latin, Arabic, and Cyrillic scripts) creates significant barriers to cross-dialectal communication and pan-Kurdish linguistic unity.limitation 90%
• The MMLU benchmark for evaluating LLMs is highly US-centric and Western-focused, with a disproportionate number of questions on North America or Europe.limitation 95%
• Civil society organizations (CSOs) can act as algorithmic auditors through socio-pragmatic red-teaming to identify where models fail to respect local norms or enforce standardization.method 80%
• Academic institutions and national language institutes are crucial for providing the sociolinguistic granularity needed to prevent the erasure of non-standard language varieties in AI development.method 85%
• There is current development of models fine-tuned for automated transcription and subtitling of South Tyrolean audio into standard German writing.technical 90%

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