Description: Model adaptation is a crucial process in the field of explainable artificial intelligence, involving the modification of an existing model to improve its performance in a new context or dataset. This process is essential to ensure that AI models are not only accurate but also understandable and applicable to various situations. Adaptation may include adjustments to the model’s parameters, the incorporation of new features, or retraining with data specific to the new context. The importance of adaptation lies in its ability to make models more robust and versatile, allowing their use in multiple applications without losing the capacity to provide clear explanations for their decisions. In a world where transparency and trust in AI are increasingly demanded, model adaptation becomes a fundamental tool for developers and data scientists, enabling them to not only enhance performance but also facilitate the interpretation of results by end users. This approach not only optimizes the model’s effectiveness but also promotes greater acceptance of artificial intelligence in critical sectors such as healthcare, finance, and security, where explainability is essential for informed decision-making.
