178 citations · 218 across the 12 of their papers we have counts for
8 papers · 1 filter
Persistent and Conversational Multi-Method Explainability for Trustworthy Financial AI
Georgios Makridis, Georgios Fatouros, John Soldatos +2
Financial institutions increasingly require AI explanations that are persistent, cross-validated across methods, and conversationally accessible to human decision-makers. We presen…
Native Explainability for Bayesian Confidence Propagation Neural Networks: A Framework for Trusted Brain-Like AI
Georgios Makridis, Georgios Fatouros, John Soldatos +2
The EU Artificial Intelligence Act (Regulation 2024/1689), fully applicable to high-risk systems from August 2026, creates urgent demand for AI architectures that are simultaneousl…
CyberAId: AI-Driven Cybersecurity for Financial Service Providers
George Fatouros, Georgios Makridis, John Soldatos +18
European financial institutions face mounting regulatory pressure while their security operations centres remain constrained not by data or staffing but by reasoning capacity: ente…
Towards Conversational AI for Human-Machine Collaborative MLOps
George Fatouros, Georgios Makridis, George Kousiouris +3
This paper presents a Large Language Model (LLM) based conversational agent system designed to enhance human-machine collaboration in Machine Learning Operations (MLOps). We introd…
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…
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…
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…
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…