  In this paper, we demonstrate the feasibility of alterfactual explanations
for black box image classifiers. Traditional explanation mechanisms from the
field of Counterfactual Thinking are a widely-used paradigm for Explainable
Artificial Intelligence (XAI), as they follow a natural way of reasoning that
humans are familiar with. However, most common approaches from this field are
based on communicating information about features or characteristics that are
especially important for an AI's decision. However, to fully understand a
decision, not only knowledge about relevant features is needed, but the
awareness of irrelevant information also highly contributes to the creation of
a user's mental model of an AI system. To this end, a novel approach for
explaining AI systems called alterfactual explanations was recently proposed on
a conceptual level. It is based on showing an alternative reality where
irrelevant features of an AI's input are altered. By doing so, the user
directly sees which input data characteristics can change arbitrarily without
influencing the AI's decision. In this paper, we show for the first time that
it is possible to apply this idea to black box models based on neural networks.
To this end, we present a GAN-based approach to generate these alterfactual
explanations for binary image classifiers. Further, we present a user study
that gives interesting insights on how alterfactual explanations can complement
counterfactual explanations.
