  Large Language Models (LLMs), such as GPT-3, have demonstrated remarkable
natural language processing and generation capabilities and have been applied
to a variety tasks, such as source code generation. This paper explores the
potential of integrating LLMs in the hazard analysis for safety-critical
systems, a process which we refer to as co-hazard analysis (CoHA). In CoHA, a
human analyst interacts with an LLM via a context-aware chat session and uses
the responses to support elicitation of possible hazard causes. In this
experiment, we explore CoHA with three increasingly complex versions of a
simple system, using Open AI's ChatGPT service. The quality of ChatGPT's
responses were systematically assessed to determine the feasibility of CoHA
given the current state of LLM technology. The results suggest that LLMs may be
useful for supporting human analysts performing hazard analysis.
