Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT
Can safety analysis make use of Large Language Models (LLMs)? A case study explores Systems Theoretic Process Analysis (STPA) applied to Automatic Emergency Brake (AEB) and Electricity Demand Side Management (DSM) systems using ChatGPT. We investigate how collaboration schemes, input semantic complexity, and prompt guidelines influence STPA results. Comparative results show that using ChatGPT without human intervention may be inadequate due to reliability related issues, but with careful design, it may outperform human experts. No statistically significant differences are found when varying the input semantic complexity or using common prompt guidelines, which suggests the necessity for developing domain-specific prompt engineering. We also highlight future challenges, including concerns about LLM trustworthiness and the necessity for standardisation and regulation in this domain.
Code (2)
Tasks
ManagementPrompt EngineeringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Automated Hazard Detection in Construction Sites Using Large Language and Vision-Language Models
This thesis explores a multimodal AI framework for enhancing construction safety through the combined analysis of textual and visual data. In safety-critical environments such as construction sites, accident data often e…
HySafe-AI: Hybrid Safety Architectural Analysis Framework for AI Systems: A Case Study
AI has become integral to safety-critical areas like autonomous driving systems (ADS) and robotics. The architecture of recent autonomous systems are trending toward end-to-end (E2E) monolithic architectures such as larg…
Autonomous DrivingFTA generation using GenAI with an Autonomy sensor Usecase
Functional safety forms an important aspect in the design of systems. Its emphasis on the automotive industry has evolved significantly over the years. Till date many methods have been developed to get appropriate FTA(Fa…
Autonomous DrivingPrompt EngineeringRECALL-MM: A Multimodal Dataset of Consumer Product Recalls for Risk Analysis using Computational Methods and Large Language Models
Product recalls provide valuable insights into potential risks and hazards within the engineering design process, yet their full potential remains underutilized. In this study, we curate data from the United States Consu…
Large Language ModelCASE-Bench: Context-Aware Safety Evaluation Benchmark for Large Language Models
Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption. Current LLM safety benchmarks often focus solely on the refusal of individual problematic queries, w…