6 papers · 1 filter
EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detection
Georgios Kaoukis, Ioannis Aris Koufopoulos, Eleni Psaroudaki +4
As AI and web agents become pervasive in decision-making, it is critical to design intelligent systems that not only support sustainability efforts but also guard against misinform…
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…
KGRAG-Ex: Explainable Retrieval-Augmented Generation with Knowledge Graph-based Perturbations
Georgios Balanos, Evangelos Chasanis, Konstantinos Skianis +1
Retrieval-Augmented Generation (RAG) enhances language models by grounding responses in external information, yet explainability remains a critical challenge, particularly when ret…
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…
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…
Counterfactual Explanations for k-means and Gaussian Clustering
Georgios Vardakas, Antonia Karra, Evaggelia Pitoura +1
Counterfactuals have been recognized as an effective approach to explain classifier decisions. Nevertheless, they have not yet been considered in the context of clustering. In this…