As machine learning models are increasingly being employed in various
high-stakes settings, it becomes important to ensure that predictions of these
models are not only adversarially robust, but also readily explainable to
relevant stakeholders. However, it is unclear if these two notions can be
simultaneously achieved or if there exist trade-offs between them. In this
work, we make one of the first attempts at studying the impact of adversarially
robust models on actionable explanations which provide end users with a means
for recourse. We theoretically and empirically analyze the cost (ease of
implementation) and validity (probability of obtaining a positive model
prediction) of recourses output by state-of-the-art algorithms when the
underlying models are adversarially robust vs. non-robust. More specifically,
we derive theoretical bounds on the differences between the cost and the
validity of the recourses generated by state-of-the-art algorithms for
adversarially robust vs. non-robust linear and non-linear models. Our empirical
results with multiple real-world datasets validate our theoretical results and
show the impact of varying degrees of model robustness on the cost and validity
of the resulting recourses. Our analyses demonstrate that adversarially robust
models significantly increase the cost and reduce the validity of the resulting
recourses, thus shedding light on the inherent trade-offs between adversarial
robustness and actionable explanations

机器学习模型在高风险环境中的应用越来越多，确保这些模型的预测不仅具有对抗性鲁棒性，而且能够向相关利益相关方进行解释变得重要。然而，这两个概念是否可以同时实现以及是否存在着它们之间的权衡是不清楚的。在本研究中，我们首次尝试研究对抗性鲁棒模型对行动性解释的影响，这些解释为最终用户提供了救济手段。我们在理论上和实证上分析了当基础模型具有对抗性鲁棒性和非鲁棒性时，最先进算法所生成的救济方案的成本（实施的便利性）和有效性（获得积极模型预测的概率）之间的差异，并针对对抗性鲁棒和非鲁棒的线性和非线性模型推导了理论界限。我们通过多个真实数据集的实证结果验证了我们的理论结果，并展示了模型鲁棒性不同程度对救济方案的成本和有效性的影响。我们的分析表明，对抗性鲁棒模型显著增加了救济方案的成本并降低了有效性，从而揭示了对抗性鲁棒性和行动性解释之间的固有权衡。

关于对抗鲁棒性和可操作解释之间的权衡

On the Trade-offs between Adversarial Robustness and Actionable  Explanations

The Right to Explanation is an important regulatory principle that allows
individuals to request actionable explanations for algorithmic decisions.
However, several technical challenges arise when providing such actionable
explanations in practice. For instance, models are periodically retrained to
handle dataset shifts. This process may invalidate some of the previously
prescribed explanations, thus rendering them unactionable. But, it is unclear
if and when such invalidations occur, and what factors determine explanation
stability i.e., if an explanation remains unchanged amidst model retraining due
to dataset shifts. In this paper, we address the aforementioned gaps and
provide one of the first theoretical and empirical characterizations of the
factors influencing explanation stability. To this end, we conduct rigorous
theoretical analysis to demonstrate that model curvature, weight decay
parameters while training, and the magnitude of the dataset shift are key
factors that determine the extent of explanation (in)stability. Extensive
experimentation with real-world datasets not only validates our theoretical
results, but also demonstrates that the aforementioned factors dramatically
impact the stability of explanations produced by various state-of-the-art
methods.

本文研究了算法决策中 “解释权利” 的问题，并提出了理论和实证分析，表明模型曲率、训练时的权重衰减参数以及数据集变化的程度是影响解释稳定性的关键因素。