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.

通过提出一种基于随机学习的框架和特征选择模块来平衡反事实解释的权衡，本研究在生成可行的反事实解释方面展现了其有效性，并表明其比基线更加多样化和高效。