Paper highlights challenges in evaluating AI explanation methods

This paper examines the shortcomings in evaluating Explainable Artificial Intelligence (XAI) methods, particularly when dealing with static and evolving data. It illustrates these challenges using the DetoxAI system for bias detection and concept unlearning, and presents a human-grounded evaluation for explaining image classification. The research also explores adapting counterfactual explanations to evolving data streams and discusses the complexities of tracking the co-evolution of data, models, and explanations. AI

IMPACT Highlights the need for robust evaluation frameworks for AI explanation methods, crucial for trustworthy AI deployment.

RANK_REASON The cluster contains an academic paper discussing research findings and methodologies in AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• CORE Recommender
• DagsHub
• DetoxAI
• EASi 2026 Workshop
• explainable AI
• Hugging Face
• IJCAI-ECAI 2026
• Jerzy Stefanowski
• Springer CCIS

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