Explanation methods need continuous re-evaluation as data and models change—static evaluation metrics miss real-world failures where explanations become outdated or misleading.
This paper examines why evaluating explanation methods in AI is harder than it seems, using image recognition and bias detection as examples. It shows how explanations break down when data changes over time (concept drift) and proposes human-centered evaluation approaches and adaptive counterfactual methods to keep explanations relevant as models evolve.