AI for Community: Preserving Culture and Tradition

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Abstract

AI for Community begins with a simple truth: artificial intelligence is now part of the evolution of human life. The question is not whether we use it, but how we shape it. This book explores how AI can preserve cultural heritage, strengthen human agency, and deepen cultural understanding through community-driven design. The concept of Cultural AI was first shared on the global stage at the AI for Good Summit in Geneva in 2019, where technologists and storytellers gathered to imagine a future where machines could learn from ancestral wisdom. From NVIDIA’s collaboration with Te Hiku Media to protect the Māori language to UNESCO’s global initiatives in cultural preservation, the book shows how Cultural AI can serve as a bridge between generations. Authored by a multigenerational, multicultural team, it highlights grounded, human-centered projects shaped by lived experience. These include the Indigenous Knowledge Graph; Howard University’s Project Elevate Black Voices, developed with Google; and Laleh AI, which preserves and extends the knowledge of the late Dr. Laleh Bakhtiar, a pioneering thinker whose work in psychology, spirituality, and translation continues to echo across time. This book interweaves Community Voices sections between chapters, featuring interviews that explore the role and implementation of AI in new arenas. Each chapter is also supported by online resources, accessible as podcasts, videos, and articles that provide multimedia ways to deepen understanding of culturally aware AI. AI for Community highlights how community-driven language preservation, storytelling, and design can empower cultures and protect their heritage. It is a vital resource for those seeking to create AI that respects and uplifts communities.

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APA

Ardalan, I. D., Banifatemi, A., Gonzalez, F., Ingram, M., Moradinezhad, R., & Williams, L. (2025). AI for Community: Preserving Culture and Tradition. AI for Community: Preserving Culture and Tradition (pp. 1–186). CRC Press. https://doi.org/10.1201/9781003517115

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