GW Trustworthy AI Initiative

AI is changing how people work, communicate, learn and make decisions. As AI becomes increasingly embedded into everyday systems–education, healthcare, business, government and more–we need clear ways to design, evaluate and guide its use. Policymakers and researchers must balance AI’s potential to transform society with potential risks and harms. The GW Trustworthy AI Initiative (GW TAI), a pan-university initiative focused on trustworthy AI in systems and for society, serves as an engine of impact-focused interdisciplinary research, preparing resilient leaders and amplifying GW’s role as a global convener. GW TAI brings together faculty and student researchers, industry partners, practitioners and other essential stakeholders to help design systems, study real-world deployments, and inform policy founded in deep scientific understanding. 

 

 

 

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GW TAI is leading the way in trustworthy AI in systems and for society.

 

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Latest News


GW TAI DTAIS research showcase

GW PhD Fellows Apply AI to Real-World Problems

September 14, 2026

A group of 9 PhD fellows from 6 disciplines spent their summer applying trustworthy AI to address real-world problems and presented their work at a recent showcase.

A slide from the workshop explaining the difference between AI chatbots and AI agents.

Building a Website in Just a Few Minutes

August 19, 2026

Prof. John Helveston and PhD candidate Pingfan Hu recently hosted an “Agentic Workflows with Claude Code” workshop to empower members of the GW community to code responsibly using AI agents.

TRAILS awarded more than $515,000 in seed funding to five multidisciplinary research teams exploring how artificial intelligence can be designed, governed and deployed in ways that earn public trust. Illustration courtesy of TRAILS.

TRAILS Awards Over $515K for Trustworthy AI Research

August 5, 2026

TRAILS has awarded just over $515,000 to multidisciplinary teams from UMD, GW, Morgan State University, and Cornell University to support trustworthy AI research.