1 citations · 1 across the 2 of their papers we have counts for
4 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…