Workforce for AI Trust Act

• Authority: United States Congress
• Parent authority: Federal government
• Jurisdiction: United States
• Collections: U.S. federal laws
• Most recent activity: Defunct
• Most recent activity date: 2025-01-03

Risk subdomain coverage

Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the risk subdomain are described.

• Governance failure
• Lack of capability or robustness
• Lack of transparency or interpretability

All other risk subdomains had no coverage in this document.

Risk Coverage Summary

The document has minimal direct coverage of specific AI risks, with primary focus on building workforce capacity for AI governance and risk management rather than addressing specific risk domains. The document implicitly relates to governance structures (6.5) and system safety (7.3, 7.4) through its emphasis on training professionals in AI risk management, testing, evaluation, and trustworthy AI practices, but does not explicitly describe the risks themselves.

Sector coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the sector are explicitly described.

• Scientific Research & Development Services
• Educational Services
• Public Administration (excl. National Security)

All other sectors had no coverage in this document.

Sectors Summary

This document does not govern AI use within specific economic sectors. Instead, it establishes federal programs for AI governance workforce development and research fellowships that will train professionals to work across all sectors. The governance applies to federal agencies (NSF and NIST) and educational institutions participating in these programs.

Legislative Status

Hard Law

Legislative Status Summary

This is a Congressional Act that amends existing federal law (National Artificial Intelligence Initiative Act of 2020 and National Institute of Standards and Technology Act) with binding legal obligations on federal agencies, using mandatory language throughout.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Summary: This is a Congressional Act ('This Act may be cited as the Workforce for AI Trust Act') that amends existing federal legislation, indicating Congress as the proposing authority.

Target

• Entity Types: AI_Governance_Actor, AI_Developer, AI_User
• Summary: The Act primarily targets federal agencies (NSF and NIST) to implement fellowship and workforce development programs. Secondary targets include educational institutions receiving awards, students/researchers receiving fellowships, and organizations that will employ the trained AI governance workforce.

Enforcer

• Entity Types: AI_Governance_Actor
• Summary: NSF and NIST are designated as the implementing agencies with authority to administer fellowship programs, establish requirements, conduct outreach, and develop standards and guidance.

Monitor

• Entity Types: AI_Governance_Actor
• Summary: NSF and NIST have implicit monitoring responsibilities through their administration of fellowship programs, merit review panels, and development of technical standards and guidance for AI risk management.

AI Lifecycle Stages

Lifecycle stages covered by the documents, as defined in the OECD Framework for the Classification of AI Systems

• Plan and Design
• Build and Use Model
• Verify and Validate
• Deploy
• Operate and Monitor

AI Lifecycle Summary

The document addresses multiple AI lifecycle stages with emphasis on Design, Build/Use, Verify/Validate, and Deploy stages through workforce development for trustworthy AI practices. It focuses on training professionals to integrate ethical and responsible practices across the entire AI development and deployment process.

Technical Scope

Terms relating to technical scope that are mentioned or implicitly referenced in the document

• AI Systems

Technical Scope Summary

The document uses the general term 'artificial intelligence systems' throughout without defining specific AI types. It does not explicitly mention AI models, frontier AI, general purpose AI, foundation models, generative AI, predictive AI, open-weight models, or compute thresholds. The focus is on AI systems broadly across all applications and domains.