Human Factors Guidance for the Design, Implementation and Evaluation of AI/ML in the Human-Automation ATC Systems

The goal of this work is to produce a highly accessible guidance document that would raise awareness of the human factors issues critical to the design and implementation of Artificial Intelligence (AI) and Machine Learning (ML) technologies for integration in FAA automation to support air traffic control (ATC) operations, and provide practical guidance on system design, implementation, integration and evaluation that will enable the FAA Air Traffic Organization to avoid pitfalls and adhere to best practices for the integration of AI/ML technologies into ATC automation. This guidance document is intended to be very practical and applied in nature, with a specific focus on ATC, providing guidance for system designers and implementation teams in the PMO. Two-Phase Approach: (Phase 1) Identify Potential Future Applications of AI/ML Technologies and Their Integration with Other Automation to Support ATC Operations. (Phase 2) Development of a Human Factors Guidance Document for the Design, Implementation, Integration and Evaluation of AI/ML Technologies and Their Integration with Other Automation to Support ATC Operations. This guidance document will be designed to: (1) Raise awareness of the human factors issues critical to the design, implementation, integration and evaluation of AI/ML technologies and their integration with other automation to support ATC operations. (2) Provide practical guidance on the design, implementation, integration and evaluation of such technologies that will enable the FAA Air Traffic Organization to avoid pitfalls and adhere to best practices for the design, implementation, integration and evaluation of AI/ML technologies in support of ATC operations. This document will consider both design and evaluation methods as well as specific guidance regarding roles and responsibilities, functionality and interface design features necessary to ensure effective human-automation interaction with such technologies.

Language

• English

Project

• Status: Active
• Funding: $913453
• Contract Numbers:

  692M152340001

• Sponsor Organizations:

  Federal Aviation Administration Office of NextGen Human Factors Division (ANG-C1)

  800 Independence Avenue SW
  Washington, DC  United States  20591
• Project Managers:

  Kaufmann, Karl

• Performing Organizations:

  The Ohio State University Research Foundation

  1960 Kenny Rd. Columbus, OH 43210-1063
  , OH   
• Principal Investigators:

  Smith, Phil

  Sarter, Nadine

  Roth, Emilie

• Start Date: 20221201
• Expected Completion Date: 20241201
• Actual Completion Date: 0
• USDOT Program: Air Traffic Control/Technical Operations Human Factors
• Subprogram: Human Factors Design Standards, Guidance, and Studies Supporting NAS Technology Integration

Subject/Index Terms

• TRT Terms: Air traffic control; Artificial intelligence; Automation; Human factors engineering; Machine learning
• Subject Areas: Aviation; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01865994
• Record Type: Research project
• Source Agency: Federal Aviation Administration
• Contract Numbers: 692M152340001
• Files: RIP, USDOT
• Created Date: Nov 30 2022 8:06AM