activity
20232026
most citedXAI for All: Can Large Language Models Simplify Explainable AI?

9 citations · 19 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG20268 cited

Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models

Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis +4

In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning mo…

cs.AI2025

VirtualXAI: A User-Centric Framework for Explainability Assessment Leveraging GPT-Generated Personas

Georgios Makridis, Vasileios Koukos, Georgios Fatouros +1

In today's data-driven era, computational systems generate vast amounts of data that drive the digital transformation of industries, where Artificial Intelligence (AI) plays a key…

cs.AI20242 cited

FairyLandAI: Personalized Fairy Tales utilizing ChatGPT and DALLE-3

Georgios Makridis, Athanasios Oikonomou, Vasileios Koukos

In the diverse world of AI-driven storytelling, there is a unique opportunity to engage young audiences with customized, and personalized narratives. This paper introduces FairyLan…

cs.AI20249 cited

XAI for All: Can Large Language Models Simplify Explainable AI?

Philip Mavrepis, Georgios Makridis, Georgios Fatouros +3

The field of Explainable Artificial Intelligence (XAI) often focuses on users with a strong technical background, making it challenging for non-experts to understand XAI methods. T…

cs.AI2023

Enhancing Explainability in Mobility Data Science through a combination of methods

Georgios Makridis, Vasileios Koukos, Georgios Fatouros +1

In the domain of Mobility Data Science, the intricate task of interpreting models trained on trajectory data, and elucidating the spatio-temporal movement of entities, has persiste…

cs.LG2023

XAI for time-series classification leveraging image highlight methods

Georgios Makridis, Georgios Fatouros, Vasileios Koukos +3

Although much work has been done on explainability in the computer vision and natural language processing (NLP) fields, there is still much work to be done to explain methods appli…