# Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions

**Authors:** Hua Shen, Tiffany Knearem, Reshmi Ghosh, Kenan Alkiek, Kundan Krishna, Yachuan Liu, Ziqiao Ma, Savvas Petridis, Yi-Hao Peng, Li Qiwei, Sushrita Rakshit, Chenglei Si, Yutong Xie, Jeffrey P. Bigham, Frank Bentley, Joyce Chai, Zachary Lipton, Qiaozhu Mei, Rada Mihalcea, Michael Terry  
**arXiv:** [2406.09264](https://arxiv.org/abs/2406.09264)  
**Date:** 2024-06-13  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2406.09264v1/

## Key points

- Recent advancements in general-purpose AI highlight the critical need for human-AI alignment to steer AI systems towards intended goals, ethical principles, and values.
- Lack of clear definitions and scopes surrounding human-AI alignment presents a significant challenge hindering collaborative efforts across research domains.
- Traditional approaches view AI alignment as a static, one-way process rather than recognizing it as an ongoing, mutual alignment issue.
- A systematic review was conducted on over 400 papers published between 2019 and January 2024 to characterize, define, and scope human-AI alignment, resulting in the introduction of the "Bidirectional Human-AI Alignment" framework.
- The framework encompasses aligning AI to humans to ensure outcomes aligned with intentions and aligning humans to AI for individual and societal adaptation to advancements in AI cognitively and behaviorally.
- Key findings from the literature analysis shed light on discussions around human values, interaction techniques, and evaluation methods within the context of human-AI alignment.
- Three primary challenges were identified along with proposed examples of potential solutions for each challenge to guide future research endeavors in this field.

## Summary

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Recent advancements in general-purpose AI have underscored the critical need to steer AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept known as alignment. However, the lack of clear definitions and scopes surrounding human-AI alignment presents a significant challenge, hindering collaborative efforts across research domains to achieve this alignment. Traditional approaches in ML- and philosophy-oriented alignment research often view AI alignment as a static, one-way process focused on ensuring that AI systems' objectives align with those of humans, rather than recognizing it as an ongoing, mutual alignment issue. This perspective overlooks the dynamic nature of alignment over time. To address these gaps comprehensively, a systematic review was conducted on over 400 papers published between 2019 and January 2024 across various domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML), among others. The review aimed to characterize, define, and scope human-AI alignment. As a result, a conceptual framework termed "Bidirectional Human-AI Alignment" was introduced to organize existing literature from a human-centric viewpoint. This framework encompasses two key aspects: firstly, conventional studies focusing on aligning AI to humans to ensure that AI generates outcomes aligned with human intentions; and secondly, a novel concept of aligning humans to AI which seeks to facilitate individual and societal adaptation to advancements in AI both cognitively and behaviorally. Moreover,<fd>key findings derived from the literature analysis shed light on discussions around human values,</fd><fd>interaction techniques,</fd><fd>and evaluation methods within the context of human-AI alignment.</fd>To guide future research endeavors in this field,<fd>three primary challenges were identified along with proposed examples of potential solutions for each challenge.</fd>By emphasizing bidirectional human-AI alignment as an essential aspect of advancing AI technologies responsibly and ethically, this comprehensive review sets the stage for further exploration into enhancing the synergy between humans and artificial intelligence systems for mutual benefit and harmonious coexistence in society.