Score Based Explanation

Score-based explanation methods aim to enhance the transparency and trustworthiness of machine learning models by assigning numerical scores to features, indicating their contribution to a model's prediction. Current research focuses on addressing limitations of existing methods, such as the assumption of feature independence (e.g., using manifold projections) and high computational cost (e.g., through approximation techniques like conformal regression), and on integrating explanations into active learning and other applications. These advancements are crucial for building more reliable and understandable AI systems, particularly in high-stakes domains where understanding model decisions is paramount, and for improving the efficiency of machine learning workflows.