| Abstract: |
| \begin{abstract}
The increasing complexity of artificial intelligence (AI) models has led to a rising demand for explainability in AI (XAI). Explainable Artificial Intelligence aims to make AI`s decision-making processes transparent and understandable to humans. This talk examines the integral connection between XAI and mathematics, highlighting how mathematical principles can enhance the interpretability, transparency, and trustworthiness of AI models. We explore the mathematical foundations that underpin XAI techniques, examine case studies where mathematics has improved explainability, and propose future directions for integrating mathematics into XAI frameworks.
\end{abstract} |
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