Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI

2025-07-17

AI Summary

To address declining model interpretability and consequent trust deficits in complex machine learning systems, this paper proposes a trustworthy XAI framework grounded in uncertainty decoupling. Methodologically, it is the first to disentangle aleatoric from epistemic uncertainty and leverages epistemic uncertainty as a reliability metric for explanations: (i) as a rejection threshold for low-fidelity explanations, and (ii) as a dynamic signal to adapt explanation strategies—e.g., feature attribution or counterfactual generation—to local uncertainty conditions. The framework integrates Bayesian neural networks, rigorous uncertainty quantification, and state-of-the-art XAI techniques. Extensive experiments across diverse models—including traditional machine learning and deep neural networks—demonstrate substantial improvements in explanation stability and robustness. Crucially, it effectively filters unreliable explanations, thereby enhancing user trust in AI-driven decisions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recent advancements in machine learning have emphasized the need for transparency in model predictions, particularly as interpretability diminishes when using increasingly complex architectures. In this paper, we propose leveraging prediction uncertainty as a complementary approach to classical explainability methods. Specifically, we distinguish between aleatoric (data-related) and epistemic (model-related) uncertainty to guide the selection of appropriate explanations. Epistemic uncertainty serves as a rejection criterion for unreliable explanations and, in itself, provides insight into insufficient training (a new form of explanation). Aleatoric uncertainty informs the choice between feature-importance explanations and counterfactual explanations. This leverages a framework of explainability methods driven by uncertainty quantification and disentanglement. Our experiments demonstrate the impact of this uncertainty-aware approach on the robustness and attainability of explanations in both traditional machine learning and deep learning scenarios.

Problem

• Enhancing AI transparency via uncertainty decomposition in predictions
• Distinguishing aleatoric and epistemic uncertainty for explanation selection
• Improving explanation robustness using uncertainty-aware frameworks

Innovation

• Decompose uncertainty into aleatoric and epistemic types
• Use epistemic uncertainty to reject unreliable explanations
• Guide explanation choice via aleatoric uncertainty quantification