Towards Human-AI Mutual Learning: A New Research Paradigm
Not on the map. Why?
Abstract (arXiv)
This paper describes a new research paradigm for studying human-AI collaboration, named "human-AI mutual learning", defined as the process where humans and AI agents preserve, exchange, and improve knowledge during human-AI collaboration. We describe relevant methodologies, motivations, domain examples, benefits, challenges, and future research agenda under this paradigm.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 38 references that are in the map. First posted on arXiv 7 May 2024.
Builds on
6 of 38 references.
References · 6
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2017Deep reinforcement learning from human preferencescited by 3,912
- 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 759
- 2017The Expressive Power of Neural Networks: A View from the Widthcited by 320
- 2020A Survey on Neural Network Interpretabilitycited by 291
- 2022Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Makingcited by 33
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.