2 citations · 2 across the 3 of their papers we have counts for
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AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions
Jamie Duell, Alejandro Jimenez Rodriguez, Mahault Albarracin
Counterfactual explanations formalize "what-if" scenarios by identifying modifications to an input instance that obtain a desired alternative prediction. Traditionally, whether gen…
Explainable Uncertainty Estimation for Reliable Medical AI
Li Rong Wang, Jamie Duell, Xinran Xu +6
Artificial intelligence has strong potential to support clinical decision-making, yet its adoption in healthcare remains limited due to a lack of trust. Uncertainty estimation can…
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…
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
Jamie Duell, Monika Seisenberger, Hsuan Fu +1
Deep Neural Networks (DNNs) stand out as one of the most prominent approaches within the Machine Learning (ML) domain. The efficacy of DNNs has surged alongside recent increases in…
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI
Jamie Andrew Duell
Explainable Artificial Intelligence (XAI) is a rising field in AI. It aims to produce a demonstrative factor of trust, which for human subjects is achieved through communicative me…