Machine learning is increasingly applied in high-stakes decision making that directly affect people's lives, and this leads to an increased demand for systems to explain their decisions. Explanations often take the form of counterfactuals, which consists of conveying to the end user what she/he needs to change in order to improve the outcome. Computing counterfactual explanations is challenging, because of the inherent tension between a rich semantics of the domain, and the need for real time response. In this paper we present GeCo, the first system that can compute plausible and feasible counterfactual explanations in real time. At its core, GeCo relies on a genetic algorithm, which is customized to favor searching counterfactual explanations with the smallest number of changes. To achieve real-time performance, we introduce two novel optimizations: $\Delta$-representation of candidate counterfactuals, and partial evaluation of the classifier. We compare empirically GeCo against four other systems described in the literature, and show that it is the only system that can achieve both high quality explanations and real time answers.

中文翻译：

---

GeCo：实时的质量反事实说明

机器学习越来越多地应用于直接影响人们生活的高风险决策中，这导致对解释其决策的系统的需求增加。解释通常采取反事实的形式，包括向最终用户传达她/他需要改变以改善结果的内容。由于领域丰富的语义与实时响应之间的内在矛盾，计算反事实解释是具有挑战性的。在本文中，我们介绍了GeCo，这是第一个可以实时计算合理可行的反事实解释的系统。GeCo的核心是遗传算法，该遗传算法经过定制，可帮助您查找变化最少的反事实解释。为了实现实时性能，我们介绍了两种新颖的优化方法：候选反事实的$ \ Delta $表示和分类器的部分评估。我们将GeCo与文献中描述的其他四个系统进行了经验比较，并表明它是唯一可以实现高质量解释和实时答案的系统。