  With the rapid development of eXplainable Artificial Intelligence (XAI), a
long line of past work has shown concerns about the Out-of-Distribution (OOD)
problem in perturbation-based post-hoc XAI models and explanations are socially
misaligned. We explore the limitations of post-hoc explanation methods that use
approximators to mimic the behavior of black-box models. Then we propose
eXplanation-based Counterfactual Retraining (XCR), which extracts feature
importance fastly. XCR applies the explanations generated by the XAI model as
counterfactual input to retrain the black-box model to address OOD and social
misalignment problems. Evaluation of popular image datasets shows that XCR can
improve model performance when only retaining 12.5% of the most crucial
features without changing the black-box model structure. Furthermore, the
evaluation of the benchmark of corruption datasets shows that the XCR is very
helpful for improving model robustness and positively impacts the calibration
of OOD problems. Even though not calibrated in the validation set like some OOD
calibration methods, the corrupted data metric outperforms existing methods.
Our method also beats current OOD calibration methods on the OOD calibration
metric if calibration on the validation set is applied.
