2 citations · 2 across the 3 of their papers we have counts for
4 papers
Augmenting The Weather: A Hybrid Counterfactual-SMOTE Algorithm for Improving Crop Growth Prediction When Climate Changes
Mohammed Temraz, Mark T Keane
In recent years, humanity has begun to experience the catastrophic effects of climate change as economic sectors (such as agriculture) struggle with unpredictable and extreme weath…
Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions
Courtney Ford, Mark T. Keane
Explainable AI (XAI) methods often struggle to generate clear, interpretable outputs for users without domain expertise. We introduce Feature-Guided Neighbor Selection (FGNS), a po…
A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers
Stephen McAleese, Mark Keane
Counterfactual explanations can be used to interpret and debug text classifiers by producing minimally altered text inputs that change a classifier's output. In this work, we evalu…
Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)
Mark T. Keane, Barry Smyth
Recently, a groundswell of research has identified the use of counterfactual explanations as a potentially significant solution to the Explainable AI (XAI) problem. It is argued th…