Using AI to support safety management – Analysing occupational safety data using machine learning within the framework of human factors

Abstract

Organisations collect large amounts of safety data concerning safety events. However, many organisations are still in their infancy in this regard, especially when it comes to utilising large datasets containing textual safety data to support their safety management. Fast-developing Artificial Intelligence (AI) and machine learning methods offer new possibilities for efficient analysis of textual data. In this paper, we discuss aspects related to using AI to support safety management. The main contribution is to examine the possibilities and challenges of using machine learning methods to analyse unstructured textual safety data from organisations’ incident reports and accident investigation texts in order to identify and classify the contributing factors. To this end, we fine-tuned a pretrained Large Language Model (LLM), using Human Factors (HF) and the HF Tool as a framework, to classify the contributing factors mentioned in report texts. The HF Tool supports the systemic analysis of safety incidents and guides the identification of contributing factors at individual, work, group/team, and organisational levels. Furthermore, the LLM developed in this study, AN-HF-classifier-V1, was evaluated on two distinct datasets comprised of excerpts, which were classified according to HF Tool by human factors experts. Our findings suggested that this classifier may support the identification of HF from large textual datasets. However, we found that the quality of textual safety data needs to be improved to include more comprehensive and accurate factors contributing to incidents. The need to improve the quality of data applies regardless of whether AI is used in the analysis or not.