# Plain Language Summary: Workforce for AI Trust Act

This bill creates new educational programs and training opportunities focused on making artificial intelligence systems safer and more trustworthy. The government will fund fellowships for graduate students and postdoctoral researchers who want to study how to build AI systems responsibly. These aren't just for computer scientists—the bill specifically encourages people from other fields like philosophy, sociology, law, and linguistics to participate, because developing trustworthy AI requires diverse perspectives. Students can receive funding for tuition, living expenses, and research costs for up to three years, and the program will actively recruit from underrepresented groups and different regions of the country.

Beyond fellowships, the bill directs the National Science Foundation to expand AI skills training across all scientific fields. This means graduate students and researchers working on any type of science project can get additional training on how to use AI tools in their work. The government will also fund workshops where researchers from different disciplines can learn together about responsible AI development and deployment. Additionally, when the National Science Foundation reviews proposals for AI research, the bill requires that review panels include experts from social sciences, ethics, law, and linguistics—not just computer scientists—to ensure proposals are evaluated from multiple angles.

The bill also tasks the National Institute of Standards and Technology with supporting workforce development for people who specialize in managing AI risks. This includes training people to test, evaluate, and verify that AI systems work safely and fairly. The government will develop standards and guidance describing what skills and knowledge these professionals need, helping create clear career pathways for people interested in AI governance and risk management roles.

Overall, this bill aims to build a larger, more diverse workforce of people trained to think critically about AI safety and ethics, both in research settings and in organizations that deploy AI systems.