44 citations · 113 across the 13 of their papers we have counts for
5 papers · 1 filter
Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence
Ana Lucic, Maurits Bleeker, Sami Jullien +2
In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the…
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…
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
Kim de Bie, Ana Lucic, Hinda Haned
In hybrid human-AI systems, users need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown. To accommodate such settings,…
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
Ana Lucic, Madhulika Srikumar, Umang Bhatt +4
Given that there are a variety of stakeholders involved in, and affected by, decisions from machine learning (ML) models, it is important to consider that different stakeholders ha…
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…