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Human-AI Teams in Aviation: Considerations from Human Factors and Team Science.

Authors :
Korentsides, Jenna
Keebler, Joseph R.
Fausett, Crystal M.
Patel, Sabina M.
Lazzara, Elizabeth H.
Source :
Journal of Aviation / Aerospace Education & Research. 2024 Special issue, Vol. 33 Issue 4, p63-73. 11p.
Publication Year :
2024

Abstract

Artificial Intelligence (AI) has transformed the way human-computer interaction (HCI) teams are able to collaborate and coordinate in various domains, including aviation. AI's transformative capabilities can enhance teamwork, efficiency, and safety, particularly in risk management. AI's ability to process vast amounts of data and provide real-time insights enables informed decision-making and automation of repetitive tasks in aviation. By combining the strengths of AI and humans, outlined in our modified version of the 'HABA-MABA' framework, a dynamic teamwork relationship emerges, provided roles are successfully allocated. AI systems are able to act as intelligent assistants, offering timely recommendations, fostering effective communication, and facilitating coordination among crew members. Its adaptability and capacity for learning improve collaboration abilities, tailoring strategies to meet the team's specific needs. This paper explores the theories, considerations, and implications of human-AI teams in aviation, highlighting potential benefits, training recommendations, and future research directions. While human-AI teams offer numerous benefits, addressing the risks, limitations, and ethical considerations is crucial to ensuring safe and efficient operations. Future research must prioritize transparency, explainability, adaptability, and real-world testing to unlock the full potential of human-AI teams and foster successful integration across diverse domains. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10651136
Volume :
33
Issue :
4
Database :
Academic Search Index
Journal :
Journal of Aviation / Aerospace Education & Research
Publication Type :
Academic Journal
Accession number :
178977938