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[2602.13675] Transferable XAI: Relating Understanding Across Domains with Explanation Transfer
Summary
The paper presents Transferable XAI, a framework that enables users to apply understanding from one AI domain to another, enhancing decision-making and explanation transfer.
Why It Matters
As AI systems become more prevalent, understanding their decisions is crucial for user trust and effective application. This framework addresses the challenge of transferring knowledge across related domains, which can improve user experience and decision-making in AI applications.
Key Takeaways
- Transferable XAI allows for the transfer of understanding across related AI domains.
- The framework utilizes a general affine transformation to relate explanations between domains.
- User studies indicate that Transferable XAI enhances understanding and decision faithfulness compared to single-domain explanations.
- This approach can reduce the cognitive burden on users by leveraging prior knowledge.
- The framework supports various domain types, improving the reusability of explanations.
Computer Science > Human-Computer Interaction arXiv:2602.13675 (cs) [Submitted on 14 Feb 2026] Title:Transferable XAI: Relating Understanding Across Domains with Explanation Transfer Authors:Fei Wang, Yifan Zhang, Brian Y. Lim View a PDF of the paper titled Transferable XAI: Relating Understanding Across Domains with Explanation Transfer, by Fei Wang and 2 other authors View PDF HTML (experimental) Abstract:Current Explainable AI (XAI) focuses on explaining a single application, but when encountering related applications, users may rely on their prior understanding from previous explanations. This leads to either overgeneralization and AI overreliance, or burdensome independent memorization. Indeed, related decision tasks can share explanatory factors, but with some notable differences; e.g., body mass index (BMI) affects the risks for heart disease and diabetes at the same rate, but chest pain is more indicative of heart disease. Similarly, models using different attributes for the same task still share signals; e.g., temperature and pressure affect air pollution but in opposite directions due to the ideal gas law. Leveraging transfer of learning, we propose Transferable XAI to enable users to transfer understanding across related domains by explaining the relationship between domain explanations using a general affine transformation framework applied to linear factor explanations. The framework supports explanation transfer across various domain types: translation for da...