This paper introduces three principles for evaluating feature-importance explanations and proposes AXE, a novel framework designed to accurately differentiate models within a Rashomon set. AXE effectively detects adversarial fairwashing, where discriminatory model behaviors are intentionally masked by misleading explanations, outperforming existing evaluation metrics.