Explainable AI offers insights into what factors drive a certain prediction of a black-box AI system. One popular interpreting approach is through counterfactual explanations, which go beyond why a system arrives at a certain decision to further provide suggestions on what a user can do to alter the original outcome. A counterfactual example must possess plenty of desiderata. These constraints exist at trade-offs between one and another presenting radical challenges to existing works. We here propose a stochastic learning-based framework that effectively balances the counterfactual trade-offs. The framework consists of a generation and a feature selection module with complementary roles: the former aims to model the distribution of valid counterfactuals whereas the latter serves to enforce additional constraints in a way that allows for differentiable training and amortized optimization. We demonstrate the effectiveness of our method in generating actionable and plausible counterfactuals that are more diverse than the existing methods and particularly more efficient than closest baselines.

翻译：可解释人工智能揭示了黑盒人工智能系统做出特定预测的驱动因素。一种流行的解释方法是通过反事实解释，它不仅解释系统为何得出某个决策，还进一步提供用户可以采取哪些行动来改变原始结果的建议。一个反事实示例必须满足众多理想特性，但这些约束之间存在权衡关系，给现有研究带来了重大挑战。本文提出了一种基于随机学习的框架，能够有效平衡反事实中的权衡。该框架由生成模块和特征选择模块组成，二者功能互补：前者旨在建模有效反事实的分布，而后者则以支持可微训练和摊销优化的方式施加额外约束。我们证明了该方法在生成可执行且合理的反事实方面优于现有方法，特别是在效率上显著优于最接近的基线方法，同时生成的反事实更具多样性。