  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, all
common approaches from this field are based on communicating information about
features or characteristics that are especially important for an AI's decision.
We argue that in order to fully understand a decision, not only knowledge about
relevant features is needed, but that the awareness of irrelevant information
also highly contributes to the creation of a user's mental model of an AI
system. Therefore, we introduce a new way of explaining AI systems. Our
approach, which we call Alterfactual Explanations, 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 characteristics of the input data can
change arbitrarily without influencing the AI's decision. We evaluate our
approach in an extensive user study, revealing that it is able to significantly
contribute to the participants' understanding of an AI. We show that
alterfactual explanations are suited to convey an understanding of different
aspects of the AI's reasoning than established counterfactual explanation
methods.
