研究论文开放获取
Enhancing Human-AI Collaboration through Adaptive Interaction and Explainability
Zhaobin Li
University of California, Irvine
来源Proceedings of the AAAI/ACM Conference on AI Ethics and Society
年份2025
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
摘要 · 完整
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.
逐年被引趋势
110
126
关键指标
1
被引次数 · OpenAlex
1.42
领域内被引倍数
同类平均 = 1
同类平均 = 1
前 24%
引用位次
同领域 · 同年份 · 同类型
同领域 · 同年份 · 同类型
0
参考文献
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
论文问答
当前基于摘要回答
可就本文提问;依据不足时会说明。
学术脉络
学科主题
计算机 / AIExplainable Artificial Intelligence (XAI)
参考文献 0
暂无参考文献明细
引用本文 1
Human–AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions
被引 1Francesco Mariotti, Laura Maria Cacioppa, Nicolò Rossini · Journal of Imaging · 2026
按被引量排序,此处列出前 3 条