  In computer vision, explainable AI (xAI) methods seek to mitigate the
'black-box' problem by making the decision-making process of deep learning
models more interpretable and transparent. Traditional xAI methods concentrate
on visualizing input features that influence model predictions, providing
insights primarily suited for experts. In this work, we present an
interaction-based xAI method that enhances user comprehension of image
classification models through their interaction. Thus, we developed a web-based
prototype allowing users to modify images via painting and erasing, thereby
observing changes in classification results. Our approach enables users to
discern critical features influencing the model's decision-making process,
aligning their mental models with the model's logic. Experiments conducted with
five images demonstrate the potential of the method to reveal feature
importance through user interaction. Our work contributes a novel perspective
to xAI by centering on end-user engagement and understanding, paving the way
for more intuitive and accessible explainability in AI systems.
