Explanation Uncertainty

Explanation uncertainty in machine learning focuses on understanding and quantifying the reliability of model explanations, aiming to improve trust and interpretability. Current research investigates methods to estimate this uncertainty, often integrating it with explanation techniques like gradient-based methods, SHAP values, and prototype-based networks, and employing approaches such as bootstrapping and Bayesian methods. This work is crucial for building trustworthy AI systems across diverse applications, from medical image analysis to autonomous driving, by providing insights into model limitations and enhancing the reliability of predictions.