4 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
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…
Probabilistic Deduction: an Approach to Probabilistic Structured Argumentation
Xiuyi Fan
This paper introduces Probabilistic Deduction (PD) as an approach to probabilistic structured argumentation. A PD framework is composed of probabilistic rules (p-rules). As rules i…
Explainable Decision Making with Lean and Argumentative Explanations
Xiuyi Fan, Francesca Toni
It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "goo…