What is it about?
As AI systems become more powerful, the risks of them developing unintended behaviors (misalignment) also increase. This paper is a comprehensive guide to "AI alignment"—the research field dedicated to making artificial intelligence systems helpful and safe by ensuring they behave in line with human intentions and values. We identify four key principles that a well-aligned AI should have: - Robustness (operates reliably in different situations) - Interpretability (we can understand its decision-making) - Controllability (humans can direct and intervene if needed) - Ethicality (it adheres to human moral standards and values)
Featured Image
Photo by Igor Omilaev on Unsplash
Why is it important?
This survey is uniquely timely because it arrives just as AI systems are becoming powerful enough to be deployed in high-stakes, real-world domains, making the problem of AI alignment more urgent than ever. What sets our work apart is the new, comprehensive, and practical framework it provides for understanding this incredibly diverse and fast-moving field. Instead of just listing different research topics, we organize the entire field into a clear, two-part "alignment cycle": - Forward Alignment: How we build aligned systems (e.g., training with human feedback). - Backward Alignment: How we check, verify, and manage these systems (e.g., safety evaluations, interpretability, and governance).
Perspectives
Working on this survey was an incredible and humbling experience. AI alignment is one of the most important and rapidly evolving challenges of our time , but it's also incredibly broad, pulling in ideas from machine learning, ethics, assurance, and even global policy. It often feels like everyone is working on a different piece of the puzzle. My personal hope for this paper is that it serves as a much-needed map for this complex territory. We brought together a large group of researchers to try and connect all these different pieces—from the technical details of 'Forward Alignment' training to the crucial, big-picture challenges of 'Backward Alignment' like safety assurance and governance.
Jiayi Zhou
Peking University
Read the Original
This page is a summary of: AI Alignment: A Contemporary Survey, ACM Computing Surveys, November 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3770749.
You can read the full text:
Resources
Contributors
The following have contributed to this page
