1 citations · 2 across the 6 of their papers we have counts for
6 papers
UniFair: A unified fair clustering approach based on separation and compactness
Antonia Karra, Vasiliki Papanikou, Georgios Vardakas +2
Clustering is increasingly used to support high-impact decisions, yet standard objectives such as k-means can produce clusterings that treat demographic groups unequally. Existing…
TACENR: Task-Agnostic Contrastive Explanations for Node Representations
Vasiliki Papanikou, Evaggelia Pitoura
Graph representation learning has achieved notable success in encoding graph-structured data into latent vector spaces, enabling a wide range of downstream tasks. However, these no…
Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration
Vasiliki Papanikou, Danae Pla Karidi, Evaggelia Pitoura +2
As Artificial Intelligence (AI) is increasingly used in areas that significantly impact human lives, concerns about fairness and transparency have grown, especially regarding their…
Health Misinformation in Social Networks: A Survey of IT Approaches
Vasiliki Papanikou, Panagiotis Papadakos, Theodora Karamanidou +3
In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims…
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…
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…