Sophisticated machine models are increasingly used for high-stakes decisions in everyday life. There is an urgent need to develop effective explanation techniques for such automated decisions. Rule-Based Explanations have been proposed for high-stake decisions like loan applications, because they increase the users' trust in the decision. However, rule-based explanations are very inefficient to compute, and existing systems sacrifice their quality in order to achieve reasonable performance. We propose a novel approach to compute rule-based explanations, by using a different type of explanation, Counterfactual Explanations, for which several efficient systems have already been developed. We prove a Duality Theorem, showing that rule-based and counterfactual-based explanations are dual to each other, then use this observation to develop an efficient algorithm for computing rule-based explanations, which uses the counterfactual-based explanation as an oracle. We conduct extensive experiments showing that our system computes rule-based explanations of higher quality, and with the same or better performance, than two previous systems, MinSetCover and Anchor.

翻译：先进的机器模型越来越多地用于日常生活中的高发决策。迫切需要为这种自动决定开发有效的解释技术。基于规则的解释建议用于贷款应用程序等高发决定,因为它们增加了用户对决定的信任。然而,基于规则的解释非常低效,无法计算,而现有系统为了取得合理的业绩而牺牲其质量。我们建议采用一种新颖的方法来计算基于规则的解释,方法是使用不同种类的解释,即反事实的解释,对此已经开发出若干有效的系统。我们证明,基于规则的解释和反事实的解释是相互双重的,然后利用这一观察来开发一种基于规则的解释的计算法,这种解释使用反事实的解释作为指针。我们进行了广泛的实验,表明我们的系统对基于规则的解释进行了更高质量的比较,并且具有与前两个系统,即MinSetCover和Anchor相同的或更好的性。