Hidden Risk

Hidden risks in various applications of artificial intelligence, particularly large language models (LLMs), are a growing concern. Current research focuses on identifying and mitigating these risks, including privacy vulnerabilities stemming from fine-tuning on generated data, the potential for adversarial attacks to subvert safety mechanisms, and biases introduced by training data or evaluation methods. These investigations utilize diverse techniques such as reinforcement learning for adversarial attack generation, deep learning for risk prediction in medical contexts, and knowledge graph construction for analyzing complex systems, highlighting the need for robust and reliable AI systems across numerous domains.