Learning to Reject Low-Quality Explanations via User Feedback

• URL: http://arxiv.org/abs/2507.12900v2
• Date: Fri, 18 Jul 2025 09:14:45 GMT
• Title: Learning to Reject Low-Quality Explanations via User Feedback
• Abstract summary: We introduce ULER (User-centric Low-quality Explanation Rejector), which learns a simple rejector from human ratings and per-feature relevance judgments.<n>Our experiments show that ULER outperforms both state-of-the-art and explanation-aware learning to reject strategies at LtX.
• Score: 19.00554619010889
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Learning predictors are increasingly being employed in high-stakes applications such as credit scoring. Explanations help users unpack the reasons behind their predictions, but are not always "high quality''. That is, end-users may have difficulty interpreting or believing them, which can complicate trust assessment and downstream decision-making. We argue that classifiers should have the option to refuse handling inputs whose predictions cannot be explained properly and introduce a framework for learning to reject low-quality explanations (LtX) in which predictors are equipped with a rejector that evaluates the quality of explanations. In this problem setting, the key challenges are how to properly define and assess explanation quality and how to design a suitable rejector. Focusing on popular attribution techniques, we introduce ULER (User-centric Low-quality Explanation Rejector), which learns a simple rejector from human ratings and per-feature relevance judgments to mirror human judgments of explanation quality. Our experiments show that ULER outperforms both state-of-the-art and explanation-aware learning to reject strategies at LtX on eight classification and regression benchmarks and on a new human-annotated dataset, which we will publicly release to support future research.

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