2 papers
cs.LG2026
Provably Robust Bayesian Counterfactual Explanations under Model Changes
Jamie Duell, Xiuyi Fan
Counterfactual explanations (CEs) offer interpretable insights into machine learning predictions by answering ``what if?" questions. However, in real-world settings where models ar…
cs.SC2024
Reconciling Explanations in Multi-Model Systems through Probabilistic Argumentation
Shengxin Hong, Xiuyi Fan
Explainable Artificial Intelligence (XAI) has become critical in enhancing the transparency and trustworthiness of AI systems, especially as these systems are increasingly deployed…