Explainable AI has become a common term in the literature, scrutinized by computer scientists and statisticians and highlighted by psychological or philosophical researchers. One major effort many researchers tackle is constructing general guidelines for XAI schemes, which we derived from our study. While some areas of XAI are well studied, we focus on uncertainty explanations and consider global explanations, which are often left out. We chose an algorithm that covers various concepts simultaneously, such as uncertainty, robustness, and global XAI, and tested its ability to calibrate trust. We then checked whether an algorithm that aims to provide more of an intuitive visual understanding, despite being complicated to understand, can provide higher user satisfaction and human interpretability.

One curious property of neural networks is the vulnerability to specific attacks, often called adversarial examples. One of the directions adversarial transferability research has taken is to focus on dataset features. The transferability of adversarials is often linked to those common global features being present or not. To validate this theory, we tested if the transferability of attacks occurs when the underlying global features of a dataset remain the same. This is because topology promises to preserve the properties of an object under continuous deformations. In this paper, we test the correlation between topological similarities using the mapper algorithm by Singh et al. to generate an approximation of the topology in a graphical manner and a distance notion provided by the NetLSD algorithm, which promises size, scale, and permutation invariance. These two algorithms allow us to show that adversarial transferability is, in fact, independent of the topological similarity of datasets. We implement our findings in https://github.com/KDD-OpenSource/Topological-Transf. This is an astounding new insight, as former theories have led us to expect that if the assumption is true that global features are relevant for transferability, those should be captured using algorithms that detect global features under only topological change- Unless, of course, the transferability and those global features are explicitly agnostic to topological change. This might point to current research regarding adversarial transferability in different directions. More specifically, we take an experimental approach using topological approximation methods to capture essential features of datasets. Past studies concerning adversarial examples show that attacks can transfer in unforeseen ways and between different neural network architectures and may produce severe vulnerabilities in sophisticated learners. However, when tackling the problem of vulnerabilities to adversarial attacks, only a few approaches find generalizable results, and by no means have we answered when and how to attack transferability can occur. This paper shows that if we limit changes in a dataset to topological permutations, the transferability of adversarial examples generated will stay the same regardless of the amount of topological change. Since acceptance of the paper, we have actually extended our implementation to other adversarial methods by simply including given code from more methods into the general implementation. The code base is also easily extendable to other datasets for further reproducibility.