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.

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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
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article

Enhancing Human-AI Collaboration through Adaptive Interaction and Explainability

Zhaobin Li
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Explainable Artificial Intelligence (XAI)
article

Enhancing Human-AI Collaboration through Adaptive Interaction and Explainability

Zhaobin Li
article en

Abstract

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.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 7(2)
University of California, Irvine (US)
Openalex Percentile: Top 99%
Explainable Artificial Intelligence (XAI)
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