Risk Taxonomy

Risk taxonomies are structured classifications of potential hazards associated with emerging technologies, particularly large language models (LLMs) and other AI systems. Current research focuses on developing comprehensive taxonomies that encompass various risk categories, from biases and safety violations to security vulnerabilities and ethical concerns, often employing natural language processing (NLP) techniques like topic modeling to analyze large datasets of user interactions and incident reports. These taxonomies are crucial for benchmarking model safety, informing the development of mitigation strategies, and ultimately promoting the responsible design and deployment of AI systems across diverse applications.