Research and Development Program on New Approaches to Data Analysis for Aviation Safety

• 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.

• Overreliance and unsafe use
• Lack of capability or robustness

All other risk subdomains had no coverage in this document.

Risk Coverage Summary

The document has minimal coverage of AI risk domains, with limited focus on system safety and failures (7.3). The document primarily establishes a research program rather than addressing specific AI risks. Coverage is concentrated on technical reliability concerns in aviation safety applications.

Sector coverage

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

• Trade, Transportation & Utilities
• Scientific Research & Development Services

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

• Public Administration (excl. National Security)

All other sectors had no coverage in this document.

Sectors Summary

The document primarily governs the Trade, Transportation and Utilities sector, specifically aviation transportation and air traffic control operations. It also has coverage of Scientific Research and Development Services through the establishment of a research consortium. The focus is on applying AI/ML to aviation safety within the National Airspace System.

Legislative Status

Hard Law

Legislative Status Summary

This is a binding legislative instrument enacted by the United States Congress that establishes mandatory obligations, authorizes appropriations, and creates enforceable requirements for federal agencies.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Entities: United States Congress
• Summary: The document is a Congressional legislative act, as indicated by the authority designation and the use of legislative language establishing a federal program.

Target

• Entity Types: AI_Governance_Actor, AI_Developer
• Entities: FAA's Consortium in Aviation Operations Research (NEXTOR III); National Aeronautics and Space Administration; FAA's Office of Safety; NextGen office; units within the FAA's Air Traffic Organization; aviation industry partners; academic institutions
• Summary: The program targets research institutions and federal agencies that will develop and test AI/ML approaches for aviation safety data analysis. These entities are both governance actors (federal agencies) and AI developers (research institutions developing new algorithms and ML methods).

Enforcer

• Entity Types: AI_Governance_Actor
• Entities: The Secretary (Department of Transportation); Federal Aviation Administration (FAA)
• Summary: The Secretary (of Transportation) has the authority to establish and oversee the program, with the FAA serving as the implementing agency through its various offices and the NEXTOR III consortium.

Monitor

• Entity Types: AI_Governance_Actor
• Entities: United States Congress; Federal Aviation Administration (FAA); Department of Transportation
• Summary: Congress monitors through appropriations oversight and the sunset provision. The FAA and Department of Transportation monitor program implementation and collaboration requirements.

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
• Operate and Monitor

AI Lifecycle Summary

The document primarily covers the Plan and Design stage by establishing a research program to investigate and develop new AI/ML approaches. It also addresses Build and Use Model through the development of new algorithms and machine learning methods, and Operate and Monitor through testing, demonstrating effectiveness, and detecting anomalies in the National Airspace System.

Technical Scope

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

• Predictive AI
• Task-specific AI

Technical Scope Summary

The document explicitly mentions machine learning and artificial intelligence methods for data analysis. It does not define specific AI model types, compute thresholds, or distinguish between frontier, general purpose, or task-specific AI. The focus is on applying AI/ML techniques to aviation safety data analysis.