Generative AI for the World:
The First Workshop on
Globalizing Tasks, Evaluations, and Systems
at COLM 2026

Announcements

(May 14, 2026) Stay tuned for our workshop via X (formerly, Twitter) at @GenAI4World.
(May 14, 2026) Feel free to reach out to the organizers at genai4world-workshop (at) googlegroups (dot) com.
(May 14, 2026) If you’re interested in nominating yourself as a potential reviewer for our workshop, please fill out this form.

Generative AI holds an unprecedented promise: for the first time, a single technology may enable communication and mutual understanding across the world’s languages, cultures, and communities. Yet the global deployment of these systems reveals how far we remain from achieving this vision. AI systems built on assumptions reflecting a narrow slice of the world’s languages, literatures, and cultures are being used by everyone, everywhere—and problems are surfacing across text, images, audio, and video (Khanuja et al., 2024). New failure modes are being revealed by novel global usage patterns. Models struggle with tasks and interaction patterns (e.g., code-switching) never anticipated by predominantly monolingual development pipelines (Oh et al., 2026; Tamkin et al., 2024). These usability issues remain invisible to standard evaluation metrics (Oh et al., 2025).

Countless variations of register, context, and cultural expectations across languages and communities exert pressure on systems operating under Western-centric design assumptions. The boundaries of these systems are always discovered not by their developers, but by users—and with the rising popularity of AI, that is the whole population of the world. That users are finding prompting in Chinese reduces token costs, or that switching languages altogether yields better results than carefully engineered prompts, should not be surprising. Bringing together these perspectives, including deployment failures observed by companies, users’ creative workarounds, and researchers’ analyses of deeper structural limitations, is critical to understanding and improving these systems.

This workshop brings together industry practitioners, researchers, and global users at a critical moment to ask: what would it take for AI to truly serve everyone? Tasks, evaluations, and systems themselves must be rethought at a global scale. We advocate for globalization, not localization—not merely adapting systems to fit diverse contexts, but allowing global diversity to shape how they are built from the ground up. Across modalities—including language, vision, and audio—we seek to identify shared patterns and transferable insights across real-world use. What global weaknesses and failure modes remain undiscovered? Which tasks require different evaluation methods across cultures, and where do standard metrics fall short? This workshop creates a space to surface, study, and extend emerging innovations and vulnerabilities across languages, modalities, and communities.