A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and Safety

被引：0
|
作者：
Falegnami, Andrea [1 ]
Tomassi, Andrea [1 ]
Corbelli, Giuseppe [2 ]
Nucci, Francesco Saverio [3 ]
Romano, Elpidio [1 ]
机构：
[1] Management Engineering Faculty, Uninettuno University, Rome,00186, Italy
[2] Psychology Faculty, Uninettuno University, Rome,00186, Italy
[3] Research and Innovation Area, Uninettuno University, Rome,00186, Italy
来源：
Applied Sciences (Switzerland) | 2024年 / 14卷 / 24期
关键词：
Featured Application: LLM integration in generating new methodologies in OHS. This paper introduces a novel generative artificial intelligence workbench specifically tailored to the field of safety sciences. Utilizing large language models (LLMs); this innovative approach significantly diverges from traditional methods by enabling the rapid development; refinement; and preliminary testing of new safety methodologies. Traditional techniques in this field typically depend on slow; iterative cycles of empirical data collection and analysis; which can be both time-intensive and costly. In contrast; our LLM-based workbench leverages synthetic data generation and advanced prompt engineering to simulate complex safety scenarios and generate diverse; realistic data sets on demand. This capability allows for more flexible and accelerated experimentation; enhancing the efficiency and scalability of safety science research. By detailing an application case; we demonstrate the practical implementation and advantages of our framework; such as its ability to adapt quickly to evolving safety requirements and its potential to significantly cut down development time and resources. The introduction of this workbench represents a paradigm shift in safety methodology development; offering a potent tool that combines the theoretical rigor of traditional methods with the agility of modern AI technologies. © 2024 by the authors;
D O I：
10.3390/app142411586
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