Explainable AI aims to improve trust and transparency, which is particularly relevant in high-risk domains. It recently gained momentum, since industries are urged to comply with new regulations, such as the EU AI Act. A valuable added benefit of XAI is the potential for further model improvement, ensuring that its predictions are right for the right reasons. In practice, it is challenging, since ground-truth labels for explanations are even harder to obtain than those for the predictions of an ML model. This is even harder in the industry, where matching an explanation of a prediction with causes of events and outcomes in the system essentially amounts to a full-fledged root cause analysis, which is very complex and cost-intensive. Using domain knowledge, we combine multimodal deep learning with XAI and propose Autonomous eXplainable Iterative Learning (AXIL), a new method of training and improving a model in a self-supervised manner, without the need for the ground truth explanation labels.