Explainability Evaluation

Explainability evaluation studies whether an explanation reflects a model’s behavior and whether people can use it to understand, verify, or act on a decision. Common approaches measure faithfulness with feature removal, perturbations, counterfactuals, or causal interventions; assess stability across inputs, models, and data shifts; and compare explanations with ground-truth annotations or domain knowledge. Human studies test comprehension, decision performance, trust, and error detection, while benchmarks increasingly combine these outcomes with computational cost and robustness. No single metric works across tasks: scores can depend on architecture and perturbation choices, and mathematical quality or fluent explanations do not reliably predict human understanding or better decisions.