Counterfactual (CF) explanations have been employed as one of the modes of explainability in explainable AI-both to increase the transparency of AI systems and to provide recourse. Cognitive science and psychology, however, have pointed out that people regularly use CFs to express causal relationships. Most AI systems are only able to capture associations or correlations in data so interpreting them as casual would not be justified. In this paper, we present two experiment (total N = 364) exploring the effects of CF explanations of AI system's predictions on lay people's causal beliefs about the real world. In Experiment 1 we found that providing CF explanations of an AI system's predictions does indeed (unjustifiably) affect people's causal beliefs regarding factors/features the AI uses and that people are more likely to view them as causal factors in the real world. Inspired by the literature on misinformation and health warning messaging, Experiment 2 tested whether we can correct for the unjustified change in causal beliefs. We found that pointing out that AI systems capture correlations and not necessarily causal relationships can attenuate the effects of CF explanations on people's causal beliefs.

本文探讨了AI系统中基于反事实进行的解释在实际应用中所带来的影响以及其引发的因果信念变化，通过实验证明了提供AI系统预测的反事实解释会影响人们对于该预测所关联的特征是否具有因果性的判断，其结果表明指出AI系统只能捕捉到相关关系而非因果关系可以减轻其影响。

AI系统的反事实解释是否会扭曲普通用户对世界因果直觉的理解？如果是，我们能纠正吗？