30 citations · 30 across the 6 of their papers we have counts for
4 papers · 1 filter
TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting
Hongnan Ma, Kevin McAreavey, Weiru Liu
Time series forecasting, while vital in various applications, often employs complex models that are difficult for humans to understand. Effective explainable AI techniques are cruc…
A User Study on Contrastive Explanations for Multi-Effector Temporal Planning with Non-Stationary Costs
Xiaowei Liu, Kevin McAreavey, Weiru Liu
In this paper, we adopt constrastive explanations within an end-user application for temporal planning of smart homes. In this application, users have requirements on the execution…
Explaining Reinforcement Learning: A Counterfactual Shapley Values Approach
Yiwei Shi, Qi Zhang, Kevin McAreavey +1
This paper introduces a novel approach Counterfactual Shapley Values (CSV), which enhances explainability in reinforcement learning (RL) by integrating counterfactual analysis with…
Modifications of the Miller definition of contrastive (counterfactual) explanations
Kevin McAreavey, Weiru Liu
Miller recently proposed a definition of contrastive (counterfactual) explanations based on the well-known Halpern-Pearl (HP) definitions of causes and (non-contrastive) explanatio…