Consumer protection rules require companies that deploy models to automate decisions in high-stakes settings to explain predictions to decision subjects. These rules are motivated, in part, by the belief that explanations can promote recourse by revealing information that decision subjects can use to contest or overturn their predictions. In practice, companies provide individuals with a list of principal reasons based on feature importance derived from methods like SHAP and LIME. In this work, we show how common practices can fail to provide recourse and propose to highlight features based on their responsiveness -- the probability that a decision subject can attain a target prediction through an arbitrary intervention on the feature. We develop efficient methods to compute responsiveness scores for any model and actionability constraints. We show that standard practices in lending can undermine decision subjects by highlighting unresponsive features and explaining predictions that are fixed.

翻译：消费者保护法规要求在高风险场景中部署模型以自动化决策的公司向决策对象解释预测结果。这些法规的部分动机在于，相信解释能够通过揭示决策对象可用于质疑或推翻其预测的信息来促进追索。在实践中，公司通常基于SHAP和LIME等方法得出的特征重要性，向个人提供主要理由列表。本研究表明，常见做法可能无法提供有效追索，并建议根据特征的响应度来突出显示特征——即决策对象通过对特征进行任意干预以获得目标预测的概率。我们开发了高效方法来计算任何模型和可操作性约束下的响应度评分。我们证明，在信贷领域的标准做法可能通过强调无响应特征并解释已固定的预测结果，从而损害决策对象的利益。