IJCAI 2025 Workshop on Explainable Artificial Intelligence (XAI)

This week, Marouane will present recent work at the Workshop on Explainable Artificial Intelligence (XAI), at IJCAI in Montréal,

Explainable Artificial Intelligence (XAI) addresses the challenge of how to communicate and explain the decision-making of AI systems. The need for explainability increases as AI systems are deployed in critical applications, raising questions such as: how should explainable AI systems be designed? What queries should AI systems be able to answer about their models and decisions? How should user interfaces communicate decision making? What types of user interactions should be supported? And how should explanation quality be assessed?

The Explainable AI (XAI) workshop at IJCAI provides a forum for discussing recent research on XAI methods, highlighting and documenting promising approaches, and encouraging further work, thereby fostering connections among researchers interested in AI, human-computer interaction, and cognitive theories of explanation and transparency. This topic is of particular importance but not limited to machine learning, AI planning, and knowledge reasoning & representation.

In addition to encouraging descriptions of original or recent contributions to XAI (i.e., theory, simulation studies, subject studies, demonstrations, applications), we will welcome contributions that: survey related work; describe key issues that require further research; or highlight relevant challenges of interest to the AI community and plans for addressing them.

The paper, Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models, is available on ArXiv.


OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (August 15, 2025). IJCAI 2025 Workshop on Explainable Artificial Intelligence (XAI). Freakonometrics. Retrieved December 6, 2025 from https://doi.org/10.58079/14hds


One thought on “IJCAI 2025 Workshop on Explainable Artificial Intelligence (XAI)”

  1. I found the blog post “IJCAI 2025 Workshop on Explainable Artificial Intelligence (XAI)” on the Freakonometrics site to be a clear and timely call for reflection on how we design AI that can be both effective and accountable. The discussion asks important questions: What should explainable AI systems be able to answer about their decisions? How should those explanations be communicated in user interfaces and interactions? What metrics define good explanations? It struck me as especially relevant because as AI expands into high‑stakes domains like healthcare, finance, and public policy, the need for transparency and interpretability isn’t just academic it’s ethical and practical.

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