Post Hoc XAI Method

Post-hoc explainable AI (XAI) methods aim to interpret the predictions of complex, "black-box" machine learning models after they are trained, primarily addressing concerns about transparency and trustworthiness. Current research focuses on evaluating the reliability and effectiveness of various post-hoc techniques, such as LIME and SHAP, often using benchmark datasets and novel metrics to assess explanation quality and user comprehension. This work is crucial for building trust in AI systems across diverse applications, particularly in high-stakes domains like medicine, where understanding model decisions is paramount for both accountability and effective knowledge discovery.