Amends the National Integrated Drought Information System to include AI, machine learning, and cloud technologies for drought monitoring and forecasting. Establishes partnerships for coordinated drought information and updates modeling to probabilistic forecasts. Authorizes specific appropriations for fiscal years 2024-2028.
All other risk subdomains had no coverage in this document.
Risk Coverage Summary
This document has minimal to no coverage of AI risk domains. It is primarily a legislative authorization for a drought information system that will incorporate AI/ML technologies as tools for monitoring and forecasting. The document does not address AI-specific risks, harms, safety measures, or governance challenges.
Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the sector are explicitly described.
Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the sector are described.
All other sectors had no coverage in this document.
Sectors Summary
This document primarily governs AI use within Public Administration (specifically NOAA and federal weather/climate agencies) for drought monitoring and forecasting. It also has implications for Agriculture through the provision of drought information services to agricultural stakeholders, and Scientific Research and Development Services through its research and modeling requirements.
Hard Law
Legislative Status Summary
This is a binding federal statute enacted by the United States Congress that amends existing law, establishes mandatory requirements for federal agencies, and authorizes specific appropriations with legal force.
Proposer
Target
Enforcer
Monitor
Lifecycle stages covered by the documents, as defined in the OECD Framework for the Classification of AI Systems
AI Lifecycle Summary
The document primarily addresses the deployment and operational monitoring stages of AI systems for drought forecasting. It mandates the advancement and deployment of AI/ML technologies and requires ongoing monitoring through observational networks. There is also coverage of the planning stage through requirements to transition to probabilistic forecasts and incorporate new modeling tools.
Terms relating to technical scope that are mentioned or implicitly referenced in the document
Technical Scope Summary
The document explicitly mentions artificial intelligence and machine learning as technologies to be deployed for drought monitoring and forecasting. It does not define these terms or distinguish between different types of AI systems, models, or capabilities. No compute thresholds, foundation models, or specific AI architectures are mentioned.
Document data from AGORA, CSET Emerging Technology Observatory.