Post Hoc

Post-hoc analysis in machine learning refers to techniques applied *after* a model is trained to improve its performance, interpretability, or robustness. Current research focuses on enhancing model explainability using methods like LIME and SHAP, improving out-of-distribution detection by combining existing methods, and addressing issues like data privacy risks associated with explanations. These advancements are crucial for building trustworthy and reliable AI systems, particularly in high-stakes applications like healthcare and autonomous systems, by increasing transparency and mitigating potential biases or vulnerabilities.