Description: Model interaction in the context of explainable AI refers to how users relate to an artificial intelligence model and how they interpret its outputs. This concept is fundamental to ensuring that AI systems are not only effective but also understandable to users. Model interaction involves users’ ability to ask questions, receive answers, and understand the reasoning behind the decisions made by the model. This is especially important in critical applications, such as healthcare or legal systems, where automated decisions can significantly impact people’s lives. Model interaction also encompasses user feedback, which can be used to improve the model’s performance and its explanatory capabilities. In this sense, the goal is to create a dialogue between the user and the system, where transparency and interpretability are key. As AI becomes more integrated into everyday life, model interaction becomes an essential aspect of fostering trust and acceptance of these technologies by users, ensuring they can understand and, if necessary, question the decisions presented to them.
