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Western Bias in AI: Why Local Models Matter for Southeast Asia
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Apr 22025
Many large language models (LLMs) are trained on data that reflects Western perspectives and the English language. But what does that mean for users in Southeast Asia, where over 1,200 languages are spoken? In this video, Elina Noor and Binya Kanitroj explore how AI models like ChatGPT, Gemini, and Claude struggle with cultural understanding, historical narratives, and identity. These blind spots aren't just small errors—they reflect a deeper issue of cultural representation in AI. Now, developers across Southeast Asia are pushing back. They’re building AI models in languages like Thai, Malay, Indonesian, and Khmer to democratize AI access and challenge the dominance of Western and Chinese models. But they face tough choices: should they train new models from scratch or fine-tune existing ones? And how do they navigate the growing geopolitical competition over AI? Watch this #CarnegieExplains to learn more. Subscribe to Carnegie Explains' YouTube channel: https://bit.ly/3ca3LNu About Elina Noor: https://carnegieendowment.org/people/... Elina Noor is a senior fellow in the Asia Program at Carnegie where she focuses on developments in Southeast Asia, particularly the impact and implications of technology in reshaping power dynamics, governance, and nation-building in the region. About Binya Kanitroj: Binya Kanitroj is a former research assistant in the Asia Program at the Carnegie Endowment for International Peace. The Carnegie Endowment advances international peace by leveraging its global network to shape debates and provide decisionmakers with independent insights and innovative ideas on the most consequential global threats and opportunities. Visit our website: https://carnegieendowment.org/ Check out Carnegie Endowment for the latest foreign policy analysis:    / carnegieendowment   Like us on Facebook:   / carnegieendowment   Follow us on Twitter:   / carnegieendow   Follow us on Instagram:   / carnegieendow   #foreignpolicy #CarnegieEndowment

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Carnegie Explains

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