System Safety Preliminary Hazard Analysis (PHA) Using Generative Artificial Intelligence

Christopher W Green

Abstract


This study investigated the capability of ChatGPT, an AI-powered generative language model, to perform hazard analysis for complex systems using the ACME Missile System as a case study. Hazard analyses generated by ChatGPT were compared to those detailed in Ericson, Clifton's 2005 publication, Hazard Analysis Techniques for System Safety, focusing on adherence to MIL-STD-882E methodologies. The research addresses general questions regarding the strengths and limitations of ChatGPT in identifying hazards, assessing risks, and proposing mitigation strategies. Through a structured evaluation, the study examines the completeness, accuracy, and alignment of ChatGPT-generated analyses with traditional techniques, identifying areas of strength, such as efficiency and innovative mitigation suggestions, alongside gaps in contextual understanding and methodological consistency. Findings highlight the potential of ChatGPT as a supplementary tool for initial hazard identification, emphasizing the importance of expert validation to ensure reliability in safety-critical applications. This research contributes to understanding AI’s role in system safety engineering and integration into existing hazard analysis frameworks.


Keywords


Systems Engineering; System Safety; Artificial Intelligence; Machine Learning; Hazard Analysis

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DOI: https://doi.org/10.53889/citj.v3i2.671

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