Explainable artificial intelligence (XAI) methods are portrayed as a remedy
for debugging and trusting statistical and deep learning models, as well as
interpreting their predictions. However, recent advances in adversarial machine
learning highlight the limitations and vulnerabilities of state-of-the-art
explanations, putting their security and trustworthiness into question. The
possibility of manipulating, fooling or fairwashing evidence of the model's
reasoning has detrimental consequences when applied in high-stakes
decision-making and knowledge discovery. This concise survey of over 50 papers
summarizes research concerning adversarial attacks on explanations of machine
learning models, as well as fairness metrics. We discuss how to defend against
attacks and design robust interpretation methods. We contribute a list of
existing insecurities in XAI and outline the emerging research directions in
adversarial XAI (AdvXAI).

该论文对 50 多篇与机器学习模型解释袭击和公正性度量有关的研究进行了简要综述，并讨论了如何防御攻击和设计稳健的解释方法。该论文提出了现有 XAI（可解释人工智能）不安全因素的列表，并概述了 Adversarial XAI（AdvXAI）的新兴研究方向。