  Reliable deployment of machine learning models such as neural networks
continues to be challenging due to several limitations. Some of the main
shortcomings are the lack of interpretability and the lack of robustness
against adversarial examples or out-of-distribution inputs. In this exploratory
review, we explore the possibilities and limits of adversarial attacks for
explainable machine learning models. First, we extend the notion of adversarial
examples to fit in explainable machine learning scenarios, in which the inputs,
the output classifications and the explanations of the model's decisions are
assessed by humans. Next, we propose a comprehensive framework to study whether
(and how) adversarial examples can be generated for explainable models under
human assessment, introducing and illustrating novel attack paradigms. In
particular, our framework considers a wide range of relevant yet often ignored
factors such as the type of problem, the user expertise or the objective of the
explanations, in order to identify the attack strategies that should be adopted
in each scenario to successfully deceive the model (and the human). The
intention of these contributions is to serve as a basis for a more rigorous and
realistic study of adversarial examples in the field of explainable machine
learning.
