This work introduces a model-agnostic and explanation-agnostic index for quantifying the temporal consistency of automated explanations, and empirically demonstrates that the proposed index facilitates the quantification and localization of temporal instability in explanation streams.
Evaluating explainable Artificial Intelligence (XAI) methods is a challenging task due to the lack of reliable evaluation procedures and, in particular, the absence of ground truth explanations. In the literature, existing evaluation approaches typically assess explanations by measuring their fidelity with respect to t...
Miquel Miró-Nicolau, F. Spinnato, Riccardo Guidotti· 0 citations
This paper addresses the limitations of Explainable Artificial Intelligence (XAI) with respect to insufficient evaluation. They are illustrated through the DetoxAI image recognition system for bias detection and concept unlearning. Then, an example of a human-grounded evaluation of methods for explaining image classifi...
It is demonstrated empirically that ProToMEx not only produces explanations of comparable fidelity to popular methods like SHAP and LIME but also drastically reduces the amortised computational cost of generating local explanations, making it highly suitable for real-time applications.
A. Georgara, Adarsh Valoor, Sarvapali D. Ramchurn· 0 citations
This tutorial provides a comprehensive, end-to-end view of LLM interpretability, transitioning from microscopic neural analysis to macroscopic application and deployment, and explores how these interpretability paradigms scale and inspire the design of frontier architectures, agentic systems, and thinking models.
Wei Zhang, Zheng-Fu He, Lu-Lu Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
Explanations of machine learning models are usually judged by criteria that are hard to compare. We propose a simpler test: if an explanation really describes how a model uses its features, it should be possible to rebuild the model's predictions from it. We turn each explanation into a predictor by reading each featur...
Scientific discovery increasingly relies on methods that are both flexible and interpretable. Traditional statistical models offer interpretability but depend on restrictive assumptions, whereas modern machine learning methods often sacrifice transparency for predictive accuracy. The Mixture-of-Experts (MoE) framework...
Jyun-Yu Chen, Ming-Chung Chang, Min Yang et al.· Journal of Statistical Theor...· 0 citations
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