Doctoral Meeting: 'Explainable AI and Bayesian Networks: Development, Explanation, and Real-World Application Insights'

In this doctoral meeting, we delve into the multifaceted realm of natural language, explainability and Bayesian Networks (BNs). We will provide a comprehensive survey of the applications of BNs in real-world scenarios and explore the utilization of Large Language Models as a powerful tool for collecting BNs structure, shedding light on their practical significance. The presentation focuses on a specific application in the medical domain, employing Explainable Artificial Intelligence techniques for Non-Alcoholic Fatty Liver Disease diagnosis use case. Furthermore, we introduce a novel method crafted to enhance the interpretability of Bayesian Networks, contributing to the evolving landscape of explainable machine learning.

Supervisors: Alberto José Bugarín Diz