Measuring the Good in XAI: A Critical Examination of Explanation Evaluation Metrics

Explainable AI (XAI) aims to make complex AI models more understandable and trustworthy. However, effectively evaluating the quality of explanations remains a significant challenge. This paper critically examines existing evaluation metrics for XAI, highlighting their limitations and identifying key areas for improvement. We discuss the importance of considering various factors, including fidelity, sparsity, comprehensibility, plausibility, user trust, and the impact of explanations on decision-making. We also emphasize the need for context-specific evaluation and the integration of human-centered perspectives. By addressing these challenges and developing more robust evaluation methodologies, we can ensure that XAI techniques are effective, trustworthy, and beneficial for users.