Generally-Occurring Model Change for Robust Counterfactual Explanations
This work addresses robustness in interpretable machine learning for users affected by algorithmic decisions, but it appears incremental as it builds on prior concepts.
The paper tackles the problem of ensuring robust counterfactual explanations under model changes by generalizing the concept of Naturally-Occurring Model Change to Generally-Occurring Model Change, providing probabilistic guarantees and theoretical results for dataset perturbations.
With the increasing impact of algorithmic decision-making on human lives, the interpretability of models has become a critical issue in machine learning. Counterfactual explanation is an important method in the field of interpretable machine learning, which can not only help users understand why machine learning models make specific decisions, but also help users understand how to change these decisions. Naturally, it is an important task to study the robustness of counterfactual explanation generation algorithms to model changes. Previous literature has proposed the concept of Naturally-Occurring Model Change, which has given us a deeper understanding of robustness to model change. In this paper, we first further generalize the concept of Naturally-Occurring Model Change, proposing a more general concept of model parameter changes, Generally-Occurring Model Change, which has a wider range of applicability. We also prove the corresponding probabilistic guarantees. In addition, we consider a more specific problem, data set perturbation, and give relevant theoretical results by combining optimization theory.