1 citations · 1 across the 4 of their papers we have counts for
5 papers
Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality
Mateusz Cedro, Marcin Chlebus
Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. We invest…
On the Importance and Evaluation of Narrativity in Natural Language AI Explanations
Mateusz Cedro, David Martens
Explainable AI (XAI) aims to make the behaviour of machine learning models interpretable, yet many explanation methods remain difficult to understand. The integration of Natural La…
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…
GraphXAIN: Narratives to Explain Graph Neural Networks
Mateusz Cedro, David Martens
Graph Neural Networks (GNNs) are a powerful technique for machine learning on graph-structured data, yet they pose challenges in interpretability. Existing GNN explanation methods…
Beyond the Black Box: Do More Complex Deep Learning Models Provide Superior XAI Explanations?
Mateusz Cedro, Marcin Chlebus
The increasing complexity of Artificial Intelligence models poses challenges to interpretability, particularly in the healthcare sector. This study investigates the impact of deep…