Counterfactual explanations (CFEs) exemplify how to minimally modify a
feature vector to achieve a different prediction for an instance. CFEs can
enhance informational fairness and trustworthiness, and provide suggestions for
users who receive adverse predictions. However, recent research has shown that
multiple CFEs can be offered for the same instance or instances with slight
differences. Multiple CFEs provide flexible choices and cover diverse
desiderata for user selection. However, individual fairness and model
reliability will be damaged if unstable CFEs with different costs are returned.
Existing methods fail to exploit flexibility and address the concerns of
non-robustness simultaneously. To address these issues, we propose a
conceptually simple yet effective solution named Counterfactual Explanations
with Minimal Satisfiable Perturbations (CEMSP). Specifically, CEMSP constrains
changing values of abnormal features with the help of their semantically
meaningful normal ranges. For efficiency, we model the problem as a Boolean
satisfiability problem to modify as few features as possible. Additionally,
CEMSP is a general framework and can easily accommodate more practical
requirements, e.g., casualty and actionability. Compared to existing methods,
we conduct comprehensive experiments on both synthetic and real-world datasets
to demonstrate that our method provides more robust explanations while
preserving flexibility.

通过 Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP) 提供更健壮的解释，同时保留灵活性，以应对多个 Counterfactual explanations 可能导致的不稳定性问题。

灵活稳健的反事实解释与最小满足扰动

Flexible and Robust Counterfactual Explanations with Minimal Satisfiable  Perturbations

As black-box models increasingly power high-stakes applications, a variety of
data-driven explanation methods have been introduced. Meanwhile, machine
learning models are constantly challenged by distributional shifts. A question
naturally arises: Are data-driven explanations robust against
out-of-distribution data? Our empirical results show that even though predict
correctly, the model might still yield unreliable explanations under
distributional shifts. How to develop robust explanations against
out-of-distribution data? To address this problem, we propose an end-to-end
model-agnostic learning framework Distributionally Robust Explanations (DRE).
The key idea is, inspired by self-supervised learning, to fully utilizes the
inter-distribution information to provide supervisory signals for the learning
of explanations without human annotation. Can robust explanations benefit the
model's generalization capability? We conduct extensive experiments on a wide
range of tasks and data types, including classification and regression on image
and scientific tabular data. Our results demonstrate that the proposed method
significantly improves the model's performance in terms of explanation and
prediction robustness against distributional shifts.

本篇论文提出了基于自监督学习思想的分布式鲁棒性解释，针对黑盒模型的应用，解释在数据分布变化时的可靠性，并通过广泛的实验验证了该方法对模型的鲁棒性提升。