XAI Method

Explainable AI (XAI) methods aim to make the decision-making processes of complex machine learning models more transparent and understandable. Current research focuses on developing robust evaluation frameworks for existing XAI techniques, including those based on feature attribution, surrogate models, and concept-based explanations, and addressing challenges like the generation of out-of-distribution samples and the impact of multicollinearity. This work is crucial for building trust in AI systems across various domains, particularly in high-stakes applications like healthcare and finance, where interpretability and accountability are paramount. The development of standardized evaluation metrics and the exploration of user-centric approaches are key areas of ongoing investigation.