44 citations · 113 across the 13 of their papers we have counts for
6 papers · 1 filter
Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy
Ana Lucic
Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users u…
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa +7
When using medical images for diagnosis, either by clinicians or artificial intelligence (AI) systems, it is important that the images are of high quality. When an image is of low…
Order in the Court: Explainable AI Methods Prone to Disagreement
Michael Neely, Stefan F. Schouten, Maurits J. R. Bleeker +1
By computing the rank correlation between attention weights and feature-additive explanation methods, previous analyses either invalidate or support the role of attention-based exp…
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei +2
Given the increasing promise of graph neural networks (GNNs) in real-world applications, several methods have been developed for explaining their predictions. Existing methods for…
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
Ana Lucic, Harrie Oosterhuis, Hinda Haned +1
Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans. Counterfactual explanations c…
Explaining Predictions from Tree-based Boosting Ensembles
Ana Lucic, Hinda Haned, Maarten de Rijke
Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community. Our work focuses on doing this by g…