Enhancing Human-AI Collaboration through Adaptive Interaction and Explainability
AI is rapidly evolving, and human-AI collaboration is becoming more prevalent. Developing robust, adaptive, and transparent models is key to improving human-AI collaboration. My research explores the intersection of AI explainability and adaptive interaction to enhance collaborative decision-making. Building on my previous work in AI explainability, adversarial robustness, and adaptive algorithms, I aim to develop adaptive interaction mechanisms that are resilient to adversarial attacks and intuitively understandable to human collaborators.
Authors
- Zhaobin Li
Institutions
- University of California, Irvine (US)
Publication Details
- Journal
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society
- Published
- 2025-01-22
- DOI
- https://doi.org/10.1609/aies.v7i2.31900
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 0.00