Recently, counterfactuals using "if-only" explanations have become very popular in eXplainable AI (XAI), as they describe which changes to feature-inputs of a black-box AI system result in changes to a (usually negative) decision-outcome. Even more recently, semi-factuals using "even-if" explanations have gained more attention. They elucidate the feature-input changes that do \textit{not} change the decision-outcome of the AI system, with a potential to suggest more beneficial recourses. Some semi-factual methods use counterfactuals to the query-instance to guide semi-factual production (so-called counterfactual-guided methods), whereas others do not (so-called counterfactual-free methods). In this work, we perform comprehensive tests of 8 semi-factual methods on 7 datasets using 5 key metrics, to determine whether counterfactual guidance is necessary to find the best semi-factuals. The results of these tests suggests not, but rather that computing other aspects of the decision space lead to better semi-factual XAI.

翻译：近年来，使用“如果-仅有”解释的反事实方法在可解释人工智能（XAI）中变得非常流行，因为它们描述了黑箱AI系统特征输入的变化如何导致（通常为负面的）决策结果的变化。更近期，使用“即使-如果”解释的半事实方法受到了更多关注。它们阐明了不会改变AI系统决策结果的特征输入变化，并具有提供更有利补救方案的潜力。部分半事实方法利用针对查询实例的反事实来引导半事实生成（即所谓的反事实引导方法），而其他方法则不依赖反事实（即所谓的无反事实方法）。在本研究中，我们通过5项关键指标对7个数据集上的8种半事实方法进行全面测试，以确定是否需要反事实引导来寻找最佳半事实。测试结果表明并非如此，反而计算决策空间的其他方面能够产生更好的半事实XAI。