AI LEAD 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

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

• Governance failure

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

• Unfair discrimination and misrepresentation
• Compromise of privacy
• AI system security vulnerabilities
• 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 coverage of risk domains, with limited focus on governance structures rather than specific AI risks. Coverage is primarily concentrated in governance failure (6.5) with basic mentions of discrimination (1.1), privacy (2.1), and security (2.2) as values to uphold. The document does not substantively address the harms or risks described in most subdomains, focusing instead on establishing organizational structures for AI governance.

Sector coverage

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

• Public Administration (excl. National Security)
• National Security

All other sectors had no coverage in this document.

Sectors Summary

This document governs AI use across all federal government agencies, which spans Public Administration excluding National Security and National Security sectors. The governance framework applies to federal agencies' internal AI operations and their delivery of services to the public across all government functions.

Legislative Status

Hard Law

Legislative Status Summary

This is a binding federal statute enacted by Congress with mandatory obligations on federal agencies, enforceable through administrative oversight and GAO reporting requirements.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Summary: The bill was introduced by Senator Peters in the U.S. Senate and would be enacted by both chambers of Congress.

Target

• Entity Types: AI_Governance_Actor
• Summary: The Act applies to federal agencies, requiring them to appoint Chief Artificial Intelligence Officers, establish AI Governance Boards, and develop AI strategies.

Enforcer

• Entity Types: AI_Governance_Actor
• Summary: The Director of OMB has authority to establish the Council, issue guidance, and oversee implementation. GAO is tasked with evaluating effectiveness and reporting to Congress.

Monitor

• Entity Types: AI_Governance_Actor
• Summary: GAO is explicitly tasked with monitoring and reporting on implementation effectiveness. Congressional committees receive reports. The CAIO Council coordinates and shares practices across agencies.

AI Lifecycle Stages

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

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

AI Lifecycle Summary

The document comprehensively covers all AI lifecycle stages from planning through operational monitoring. It explicitly addresses design, data processes, model building, verification, deployment, and ongoing monitoring through requirements for AI strategies, governance boards, and risk management processes.

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 term 'artificial intelligence' throughout but does not explicitly define or distinguish between AI models, AI systems, or specific categories like frontier AI, general purpose AI, foundation models, or generative AI. It references the definition from the John S. McCain National Defense Authorization Act. No compute thresholds or open-weight model distinctions are mentioned.