3 papers
cs.AI2025
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…
cs.AI2025
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…
cs.CL2024
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…