这篇论文提出了解释卡，能帮你避免被算法解释误导，而且用SHAP和反事实解释举了具体例子。
论文提出解释卡（Explanation Cards）来增强算法解释的实用性，通过补充鲁棒性和有效性信息以及明确解释指南。以反事实解释和SHAP为例展示构建方法。解释卡将正确解读的责任从用户转移至提供者。该方案可操作化欧盟AI法案的可解释性条款。
We Need Explanation Cards to Connect Explanation Algorithms to the Real World
Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short. First, the meaning of algorithmic explanations is often not what one might intuitively expect, so expert knowledge is required to interpret them correctly. Second, recent work has shown that popular explanation algorithms are uninformative about the behavior of complex decision functions. Together, these issues create a gap between what explanations appear to convey and what they actually provide. In this work, we propose Explanation Cards for Explanation Algorithms, which augment standard explanations with complementary information about robustness and validity, as well as clear instructions for interpretation. The complementary information can render otherwise uninformative explanations practically useful, while also helping to detect cases where they are not. Importantly, the interpretation instructions in explanation cards shift responsibility from users to providers: Rather than expecting users to recognize what can and cannot be concluded from an explanation, providers must make this explicit upfront. Using counterfactual explanations and SHAP as examples, we demonstrate how providers can construct explanation cards and that these cards provide users with the guidance needed for sound interpretation. We further argue that explanation cards offer a practical means of operationalising the explainability provisions of the EU AI Act. Overall, explanation cards are a significant step toward making explanation algorithms fit for real-world use cases.