The efficiency–verification trade-off in large language model-assisted workplace risk assessment

Abstract

Large language models (LLMs) are increasingly adopted in occupational health and safety (OHS) to accelerate workplace risk assessment, yet their integration raises a dilemma between efficiency and verification. The study examines this trade-off in an LLM-assisted workflow using data from 111 student assignment files from an educational setting, analyzed at the session level, yielding 96‒121 analytic sessions depending on measure availability. Efficiency was measured via perceived time savings versus an estimated manual baseline, while the verification-burden proxy was operationalized as an AI‒human disagreement in quantitative scoring (probability and impact on 1‒5 scales). User acceptance was captured via the utility and satisfaction / intention to reuse Likert scales. The results show substantial perceived efficiency gains: the median LLM-assisted time was 20 minutes versus the median manual time of 120 minutes (median saving 0.84). The verification-burden proxy remained non-trivial: median disagreement was 0.50, and 26.8% of sessions exhibited disagreement ≥1.0. Acceptance remained high (mean satisfaction 4.06/5). Notably, 40.6% of the sessions showed a possible overreliance pattern, with high satisfaction coexisting with high disagreement. In a robust ordinary least squares (OLS) regression, utility was the strongest predictor of satisfaction, while disagreement reduced satisfaction; time saving was not significant. Perceived speed gains may thus coexist with hidden assurance costs and possible trust miscalibration, highlighting the need for verification structures and targeted training for sustainable digital safety governance.