Robust Counterfactuals

Robust counterfactual explanations aim to provide reliable and stable explanations for machine learning model predictions, ensuring that these explanations remain valid even under model perturbations or updates. Current research focuses on developing algorithms and metrics to generate such robust explanations, particularly for neural networks, tree-based ensembles, and graph neural networks, often employing techniques like diversity-based selection and stability measures. This work is crucial for enhancing the trustworthiness and reliability of explainable AI (XAI) systems, improving user understanding of model decisions and mitigating the risks associated with unreliable explanations in high-stakes applications.