4 citations · 6 across the 4 of their papers we have counts for
6 papers
On the Definition and Detection of Cherry-Picking in Counterfactual Explanations
James Hinns, Sofie Goethals, Stephan Van der Veeken +2
Counterfactual explanations are widely used to communicate how inputs must change for a model to alter its prediction. For a single instance, many valid counterfactuals can exist,…
Aggregating Local Saliency Maps for Semi-Global Explainable Image Classification
James Hinns, David Martens
Deep learning dominates image classification tasks, yet understanding how models arrive at predictions remains a challenge. Much research focuses on local explanations of individua…
Cash or Comfort? How LLMs Value Your Inconvenience
Mateusz Cedro, Timour Ichmoukhamedov, Sofie Goethals +3
Large Language Models (LLMs) are increasingly proposed as near-autonomous artificial intelligence (AI) agents capable of making everyday decisions on behalf of humans. Although LLM…
How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives
Timour Ichmoukhamedov, James Hinns, David Martens
A rapidly developing application of LLMs in XAI is to convert quantitative explanations such as SHAP into user-friendly narratives to explain the decisions made by smaller predicti…
Exposing Image Classifier Shortcuts with Counterfactual Frequency (CoF) Tables
James Hinns, David Martens
The rise of deep learning in image classification has brought unprecedented accuracy but also highlighted a key issue: the use of 'shortcuts' by models. Such shortcuts are easy-to-…
Tell Me a Story! Narrative-Driven XAI with Large Language Models
David Martens, James Hinns, Camille Dams +2
In many AI applications today, the predominance of black-box machine learning models, due to their typically higher accuracy, amplifies the need for Explainable AI (XAI). Existing…