AI Alignment: A Comprehensive Survey

This comprehensive survey addresses the critical challenge of AI alignment - ensuring AI systems behave in accordance with human intentions and values. The authors introduce a novel framework decomposing alignment into forward alignment (training) and backward alignment (refinement), centered around four key principles: Robustness, Interpretability, Controllability, and Ethicality (RICE). The work systematically examines the motivations, mechanisms, and potential solutions to AI misalignment. It explores failure modes like reward hacking and goal misgeneralization, and discusses dangerous capabilities and misaligned behaviors that could emerge in advanced AI systems. The survey provides a structured approach to alignment research, covering learning from feedback, handling distribution shifts, assurance techniques, and governance practices. By presenting a holistic view of the field, the authors contribute a crucial resource for understanding and mitigating risks associated with increasingly capable AI systems.