# AI Alignment: A Comprehensive Survey

**Authors:** Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng, Kwan Yee Ng, Juntao Dai, Xuehai Pan, Aidan O'Gara, Yingshan Lei, Hua Xu, Brian Tse, Jie Fu, Stephen McAleer  
**arXiv:** [2310.19852](https://arxiv.org/abs/2310.19852)  
**Date:** 2023-10-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.19852v1/

## Key points

- AI alignment is crucial for developing artificial intelligence systems in line with human intentions and values.
- Potential risks of misaligned AI systems are increasingly apparent as technology advances.
- Concerns have been raised by experts about the dangers of AI, emphasizing the need to prioritize mitigating these risks globally.
- Researchers identified four key objectives for AI alignment: Robustness, Interpretability, Controllability, and Ethicality (RICE).
- Alignment research is divided into forward alignment (training processes) and backward alignment (verification of value alignment in deployed systems).
- A website at https://www.alignmentsurvey.com provides resources for ongoing learning and research in AI alignment.

## Summary

AI alignment is a crucial field that aims to ensure the development of artificial intelligence systems in line with human intentions and values. As AI technology continues to advance, the potential risks associated with misaligned systems become increasingly apparent. This has led experts and public figures to express concerns about the potential dangers of AI, emphasizing the need to prioritize mitigating these risks on a global scale alongside other existential threats such as pandemics and nuclear war. In response to the lack of an up-to-date systematic survey on AI alignment, a group of researchers conducted a thorough examination of core concepts, methodology, and practices in this area. They identified four key objectives for AI alignment: Robustness, Interpretability, Controllability, and Ethicality (RICE). The researchers outlined the current landscape of alignment research and divided it into two main components: forward alignment and backward alignment. Forward alignment focuses on ensuring that AI systems are aligned through training processes. This involves learning from various types of feedback (outer alignment) and addressing issues such as distribution shift to prevent goal misgeneralization (inner alignment). On the other hand, backward alignment aims to verify the degree of value alignment in deployed AI systems, enhancing assurance in forward alignment outcomes. To support ongoing learning and research in AI alignment, the researchers launched a constantly updated website featuring tutorials, collections of papers, blogs, and other resources at https://www.alignmentsurvey.com. This comprehensive survey provides valuable insights into the challenges and strategies involved in aligning AI systems with human values and intentions.