I am dreading our LLM-written incident report future (surfingcomplexity.blog)

In a recent post, Braithwaite expressed concern over the increasing reliance on large language models (LLMs) to generate incident reports, highlighting the dangers of such a shift. While LLMs can effectively assist in gathering and organizing information for reports, using them to write the reports themselves could lead to a decline in critical thinking and a deeper understanding of incidents. Braithwaite warns that when individuals bypass the rigorous process of writing and synthesizing information, they risk producing superficially accurate reports that miss crucial nuances, potentially spreading misconceptions and diminishing learning opportunities. This trend is particularly alarming for the AI/ML community, as the fidelity of incident reporting is essential for understanding system failures and improving future responses. Unlike coding tasks, where errors are often identifiable through testing, the inaccuracies in LLM-generated reports may go unnoticed, as there’s no immediate feedback on their correctness. Consequently, as organizations may feel compelled to leverage AI tools to streamline report generation, there is a significant risk that genuine insights and lessons from incidents will be lost, undermining the critical learning process necessary for system improvement and innovation.