Modernizing Data Practices to Improve Government 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.

• Compromise of privacy
• AI system security vulnerabilities
• 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

This document has minimal risk domain coverage, primarily addressing governance structures and data management practices rather than specific AI risks. The document focuses on establishing administrative frameworks for data governance and AI adoption within federal agencies, with limited attention to risk mitigation. Coverage is concentrated in governance failure (6.5) with minimal mentions of security vulnerabilities (2.2) and system robustness (7.3).

Sector coverage

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

• Public Administration (excl. National Security)

All other sectors had no coverage in this document.

Sectors Summary

This document exclusively governs the Public Administration sector, specifically federal agencies and their use of AI and data governance practices. It does not regulate private sector entities or other economic sectors, but rather establishes internal governance frameworks for federal government operations.

Legislative Status

Hard Law

Legislative Status Summary

This is a binding federal statute enacted by the United States Congress with mandatory obligations for federal agencies, including requirements for reports, council establishment, and data governance practices.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Summary: The document is a federal bill enacted by Congress, as indicated by the standard legislative enactment clause and the bill structure.

Target

• Entity Types: AI_Governance_Actor
• Summary: The Act applies to federal agencies and establishes requirements for the Chief Data Officer Council and various agency officials to improve data governance and AI adoption practices.

Enforcer

• Entity Types: AI_Governance_Actor
• Summary: The Director has authority to issue guidance to agencies based on Council reports. The Comptroller General conducts evaluations, and Congressional committees receive reports for oversight.

Monitor

• Entity Types: AI_Governance_Actor
• Summary: The Council monitors and reports on data governance and AI use across agencies. The Comptroller General evaluates the Council's effectiveness. Congressional committees receive biennial reports.

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 addresses multiple AI lifecycle stages with primary focus on deployment and operational monitoring. It covers planning through requirements for data governance policies and infrastructure, and extensively addresses monitoring through biennial reporting and evaluation requirements. The document also touches on data collection and model building through recommendations for training data practices.

Technical Scope

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

• AI Models
• AI Systems

Technical Scope Summary

The document explicitly defines and covers artificial intelligence by reference to existing federal legislation. It addresses AI systems broadly in the context of federal agency operations, including training, testing, and operation. The document does not distinguish between different types of AI (frontier, general purpose, task-specific, generative, predictive) or mention compute thresholds or open-weight models. It does address synthetic data practices.