1 citations · 1 across the 5 of their papers we have counts for
5 papers
GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering
Christos Fragkathoulas, Eleni Psaroudaki, Themis Palpanas +1
Time-series clustering is a fundamental tool for pattern discovery, yet existing explainability methods, primarily based on feature attribution or metadata, fail to identify the tr…
UGCE: User-Guided Incremental Counterfactual Exploration
Christos Fragkathoulas, Evaggelia Pitoura
Counterfactual explanations (CFEs) are a popular approach for interpreting machine learning predictions by identifying minimal feature changes that alter model outputs. However, in…
FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness
Christos Fragkathoulas, Vasiliki Papanikou, Evaggelia Pitoura +1
Counterfactual explanations assess unfairness by revealing how inputs must change to achieve a desired outcome. This paper introduces the first graph-based framework for generating…
Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs
Christos Fragkathoulas, Odysseas S. Chlapanis
This paper introduces a novel task to assess the faithfulness of large language models (LLMs) using local perturbations and self-explanations. Many LLMs often require additional co…
On Explaining Unfairness: An Overview
Christos Fragkathoulas, Vasiliki Papanikou, Danae Pla Karidi +1
Algorithmic fairness and explainability are foundational elements for achieving responsible AI. In this paper, we focus on their interplay, a research area that is recently receivi…