Bidirectional Human-AI Alignment: Emerging Challenges and Opportunities
Hua Shen, Tiffany Knearem, Reshmi Ghosh, Michael Xieyang Liu, Andrés Monroy-Hernández, Tongshuang Wu, Diyi Yang, Yun Huang, Tanushree Mitra, Yang Li, and Marti Hearst. 2025. Bidirectional Human-AI Alignment: Emerging Challenges and Opportunities. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’25). Association for Computing Machinery, New York, NY, USA, Article 857, 1–6. https://doi.org/10.1145/3706599.3716291
Abstract
Recent advancements in general-purpose AI have highlighted the urgent need to align AI systems with the goals, ethical principles, and values of individuals and society. Existing alignment research has been primarily approached as an AI-centered, static, and unidirectional process. However, this unidirectional perspective falls short of taking into account the dynamic and evolving interaction between humans and AI, necessitating a shift toward a bidirectional, interconnected mode of human-AI alignment. This SIG aims to outline the emerging areas of bidirectinoal human-AI alignment research, propose a blueprint of future goals and challenges for fundamental alignment research, and establish a shared platform to bring together experts from HCI, AI, social sciences, and more to advance interdisciplinary research and collaboration on human-AI alignment.