AI Safety and Security

Abstract

The chapter “AI Safety and Security” presents a comprehensive and multi-dimensional exploration, addressing the critical aspects of safety and security in the context of large language models. The chapter begins by identifying the risks and threats posed by LLMs, delving into vulnerabilities such as bias, misinformation, and unintended AI interactions, impacts like privacy concerns. Building on these identified risks, it then explores the strategies and methodologies for ensuring AI safety, focusing on principles like robustness, transparency, and accountability and discussing the challenges of implementing these safety measures. It concludes with an insight into long-term AI safety research, highlighting ongoing efforts and future directions to sustain AI system safety amidst rapid technological advancements and encouraging a collaborative approach among various stakeholders. By integrating perspectives from computer science, ethics, law, and social sciences, the chapter provides an insightful and comprehensive analysis of current and future challenges in AI safety and security.