XAI Model

Explainable Artificial Intelligence (XAI) aims to make the decision-making processes of complex AI models transparent and understandable, addressing concerns about their "black box" nature, particularly in high-stakes applications like healthcare and finance. Current research emphasizes rigorous, model-based explanation methods, such as logic-based approaches and those leveraging feature attribution techniques (e.g., SHAP, LIME), with a focus on improving the accuracy, efficiency, and user-friendliness of explanations. The development and validation of robust XAI methods are crucial for building trust in AI systems and facilitating their responsible deployment across various scientific and practical domains.