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 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“如果当时”解释的反事实方法在可解释人工智能（XAI）中非常流行，因为它们描述了黑盒人工智能系统的特征输入中哪些变化会导致（通常为负面的）决策结果发生变化。更近期，使用“即使当时”解释的半事实方法获得了更多关注。它们阐明了不会改变人工智能系统决策结果的特征输入变化，并有可能提出更有益的补救措施。某些半事实方法使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），而其他方法则不使用（即所谓的无反事实方法）。本文中，我们对7个数据集上的8种半事实方法进行了全面测试，使用了5个关键指标，以确定是否必须借助反事实引导才能找到最佳半事实。测试结果表明并非如此，相反，对决策空间其他方面的计算能够带来更好的半事实XAI解释。