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Workshop
ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
Quanshi Zhang · Tian Han · Lixin Fan · Zhanxing Zhu · Hang Su · Ying Nian Wu

Fri Jul 23 05:00 AM -- 02:30 PM (PDT) @ None
Event URL: https://icml2021-xai.github.io/ »

The proposed workshop pays a special interests in theoretic foundations, limitations, and new application trends in the scope of XAI. These issues reflect new bottlenecks in the future development of XAI, for example: (1) no theoretic definition of XAI and no solid and widely-used formulation for even a specific explanation task. (2) No sophisticated formulation of the essence of ``semantics'' encoded in a DNN. (3) How to bridge the gap between connectionism and symbolism in AI research has not been sophisticatedly explored. (4) How to evaluate the correctness and trustworthiness of an explanation result is still an open problem. (5) How to bridge the intuitive explanation (e.g., the attribution/importance-based explanation) and a DNN's representation capacity (e.g., the generalization power) is still a significant challenge. (6) Using the explanation to guide the architecture design or substantially boost the performance of a DNN is a bottleneck. Therefore, this workshop aims to bring together researchers, engineers as well as industrial practitioners, who concern about the interpretability, safety, and reliability of artificial intelligence. In this workshop, we hope to use a broad discussion on the above bottleneck issues to explore new critical and constructive views of the future development of XAI. Research outcomes are also expected to profoundly influences critical industrial applications such as medical diagnosis, finance, and autonomous driving.

Accepted papers: https://arxiv.org/html/2107.08821

Author Information

Quanshi Zhang (Shanghai Jiao Tong University)
Tian Han (Stevens Institute of Technology)
Lixin Fan (WeBank)
Zhanxing Zhu (Peking University)
Hang Su (Tsinghua University)
Ying Nian Wu (UCLA)

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