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Author Barry Kirwan |
| Abstract Artificial Intelligence (AI) holds great promise for all industries in improving safe performance and efficiency, and civil aviation is no different. AI can potentially offer efficiency improvements to reduce delays and aviation’s carbon footprint, while adding safety support inside the cockpit, enabling single pilot operations and the handling of drone operations in urban environments. The European Union Aviation Safety Agency (EASA) has proposed six categories of Human-AI teaming, from machine learning support to fully autonomous AI. While AI support may in some cases be treated as ‘just more automation’, one category in particular, Collaborative AI (category 2B) considers the case of AI as an autonomous ‘team-mate’, able to take initiative, negotiate, reprioritise and execute tasks. This category pushes the envelope when it comes to contemporary Human Factors evaluation of human work systems. The question arises, therefore, of whether Human Factors is sufficiently well equipped to support the evaluation and performance assurance of such new concepts of operation, or whether we need new techniques and even new frameworks for Human-AI teaming design and assessment. Four future Human-AI Teaming use cases are considered to help gauge where Human Factors remains fit-for-purpose, where it can be modified to be so, and where we may need entirely new techniques of performance assessment and assurance. |
