Explanation Based

Explanation-based methods aim to enhance the transparency and trustworthiness of machine learning models by providing insights into their decision-making processes. Current research focuses on developing robust and faithful explanation methods, evaluating their quality using statistical measures and information theory, and integrating explanations into model training to improve both accuracy and interpretability. This work is crucial for building trust in AI systems across various domains, from medical diagnosis to autonomous driving, by providing users with understandable justifications for model predictions and facilitating the identification and mitigation of biases.