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Author Declan Saunders, Kyle Hu & Wen-Chin Li |
| Abstract This study investigates the feasibility of a generative-pre-trained transformer (GPT) to analyse aviation accident reports related to decision error, based on the Human Factors Analysis and Classification System (HFACS) framework. The application of artificial intelligence (AI) combined with machine learning (ML) is expected to expand significantly in aviation. It will have an impact on safety management and accident classification and prevention based on the development of the large language model (LLM) and prompt engineering. The results have demonstrated that there are challenges to using AI to classify accidents related to pilots’ cognitive processes, which might have an impact on pilots’ decision-making, violation, and operational behaviours. Currently, AI tends to misclassify causal factors implicated by human behaviours and cognitive processes of decisionmaking. This research reveals the potential of AI's utility in initial quick analysis with unexpected and unpredictable hallucinations, which may require a domain expert’s validation. |
