Year: 2025
Type: article
Abstract: Monitoring workplace activities is critical for ensuring job safety. Generative Artificial Intelligence (Gen-AI) and Human-centered Artificial Intelligence (Hum-AI) can suggest new trustworthy solutions to automate these monitoring procedures, ensuring improved work accident prevention. In this paper, we present a novel framework that combines Retrieval Augmented Generation (RAG) with explainable LLMs to automatically generate job safety reports from unstructured accident descriptions. Our method integrates embeddings like BERT and SciBERT and explainable AI exploiting Layer-Wise Relevance Propagation (LRP) to highlight root causes of accidents within the generated reports. We evaluate multiple LLMs, including LLaMA 3.1, Mixtral-8x7B, and DeepSeek v2, on the Aviation Safety Reporting System (ASRS) dataset. Results show that our best configuration (Mixtral-8x7B with SciBERT) achieves F1-scores up to 0.909 and GLEU and METEOR scores above 0.3 and 0.2. These findings demonstrate the effectiveness and interpretability of the proposed system in real-world job safety contexts and how the proposed approach could assist safety experts or inspectors more explicitly.
Source: Information Systems Frontiers
Institutions: University of Sannio, Unitelma Sapienza University, Sapienza University of Rome, Institute of Materials for Electronics and Magnetism, National Research Council, University of Naples Federico II, Università degli Studi eCampus, Istituto Nazionale per l'Assicurazione Contro gli Infortuni sul Lavoro
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
OpenAlex ID: W4413177808