论文标题
让您的朋友关闭和反事实近距离：在抽象环境中从最接近而不是合理的反事实解释中改进了学习
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting
论文作者
论文摘要
反事实解释（CFE）强调了模型输入的变化将以特定的方式改变其预测。 CFE已获得了可解释人工智能（XAI）的心理扎根的解决方案。最近的创新介绍了自动生成的CFE的计算合理性概念，从而通过独家创建合理的解释来增强其鲁棒性。但是，这种限制对用户体验和行为的实际好处尚不清楚。在这项研究中，我们评估了针对新手用户的迭代学习设计中计算合理CFE的客观和主观可用性。我们依靠一种类似游戏的实验设计，围绕抽象场景旋转。我们的结果表明，新手用户实际上从接收计算上的合理而不是最接近的CFE中受益于最小变化，从而导致所需结果。赛后调查中的回答表明，两组之间的主观用户经验没有差异。遵循心理合理性是比较的相似性，这可以通过以下事实来解释：与计算上合理的同步物相比，最接近条件的用户在心理上更为合理。总而言之，我们的工作强调了计算合理性和心理合理性的定义的差异，批判性地确认了在XAI方法的设计阶段已经结合了人类行为，偏好和心理模型的需求。为了可重复的研究，所有源代码，获取的用户数据和当前研究的评估脚本都可以使用：https：//github.com/ukuhl/plausiblealienzoo
Counterfactual explanations (CFEs) highlight what changes to a model's input would have changed its prediction in a particular way. CFEs have gained considerable traction as a psychologically grounded solution for explainable artificial intelligence (XAI). Recent innovations introduce the notion of computational plausibility for automatically generated CFEs, enhancing their robustness by exclusively creating plausible explanations. However, practical benefits of such a constraint on user experience and behavior is yet unclear. In this study, we evaluate objective and subjective usability of computationally plausible CFEs in an iterative learning design targeting novice users. We rely on a novel, game-like experimental design, revolving around an abstract scenario. Our results show that novice users actually benefit less from receiving computationally plausible rather than closest CFEs that produce minimal changes leading to the desired outcome. Responses in a post-game survey reveal no differences in terms of subjective user experience between both groups. Following the view of psychological plausibility as comparative similarity, this may be explained by the fact that users in the closest condition experience their CFEs as more psychologically plausible than the computationally plausible counterpart. In sum, our work highlights a little-considered divergence of definitions of computational plausibility and psychological plausibility, critically confirming the need to incorporate human behavior, preferences and mental models already at the design stages of XAI approaches. In the interest of reproducible research, all source code, acquired user data, and evaluation scripts of the current study are available: https://github.com/ukuhl/PlausibleAlienZoo
