AI Alignment: A Comprehensive Survey
Paper · 2023 · Jiaming Ji, Tianyi Qiu, Boyuan Chen et al.
Summary
As AI systems grow more capable and enter high-stakes domains, systems whose goals drift away from what people actually want become more dangerous. This survey, led by Peking University with co-authors from Cambridge, Oxford, HKUST and other institutions, maps the alignment field for newcomers and specialists alike. It first explains where misalignment comes from, pointing to mechanisms such as reward hacking and goal misgeneralization. It then sets out four objectives, abbreviated RICE: Robustness, Interpretability, Controllability, and Ethicality. The authors organize existing research into an "alignment cycle" with two halves. Forward alignment covers training: learning from feedback (preference modeling, RLHF, scalable oversight) and learning under distribution shift. Backward alignment covers assurance (safety evaluation, interpretability, verification of human values) and governance by governments, industry and third parties. The paper offers no new model, dataset or benchmark. Its contribution is a unified taxonomy and a companion website that is kept up to date. Together they serve as a structured entry point into a fast-moving and fragmented literature.
Key points
- RICE framework. Robustness, Interpretability, Controllability and Ethicality are the core alignment goals. The research areas map onto these goals many-to-many, not one-to-one.
- Forward alignment.
- Covers RLHF and its generalization to RLxF.
- Scalable-oversight proposals include Iterated Distillation and Amplification, Recursive Reward Modeling, Debate, Cooperative Inverse Reinforcement Learning, Circuit Breaking and Weak-to-Strong Generalization.
- Distribution-shift remedies are split into algorithmic ones (cross-distribution aggregation, mode connectivity) and data-based ones (adversarial and cooperative training).
- Backward alignment.
- Assurance includes safety benchmarks, red teaming, a warning about "safetywashing", intrinsic vs. post hoc interpretability, and human values verification.
- Governance follows a multi-stakeholder approach, with open problems in international and open-source governance.
- Resource. The companion site www.alignmentsurvey.com collects tutorials, paper lists and blog posts.
- Scope. The bibliography has 838 references. The arXiv record is 2310.19852 (v6, April 2025), and the survey is continually revised.
Institutions
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSocial Sciences · 1,772 worksSafety Research · 543 works
💡 Concepts 1Key ideas and methods explained on our concept pages.Artificial intelligence · 49 works
Cite
Cite as Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Lukas Vierling, Donghai Hong, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng, Juntao Dai, Xuehai Pan, Kwan Yee Ng, Aidan O'Gara, Hua Xu, Brian Tse, Jie Fu, Stephen McAleer, Yaodong Yang, Yizhou Wang, Song-Chun Zhu, Yike Guo and Wen Gao. 2023. AI Alignment: A Comprehensive Survey. arXiv:2310.19852.
@misc{ji2023ai,
title = {{AI Alignment: A Comprehensive Survey}},
author = {Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Lukas Vierling, Donghai Hong, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng, Juntao Dai, Xuehai Pan, Kwan Yee Ng, Aidan O'Gara, Hua Xu, Brian Tse, Jie Fu, Stephen McAleer, Yaodong Yang, Yizhou Wang, Song-Chun Zhu, Yike Guo and Wen Gao},
year = {2023},
doi = {10.48550/arxiv.2310.19852},
eprint = {2310.19852},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2310.19852}
}References
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