On the Trade-offs between Adversarial Robustness and Actionable
  Explanations
Satyapriya Krishna Chirag Agarwal Himabindu Lakkaraju
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