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

中图分类号：

学科分类号：

摘要：

引用

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