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Hypercomplex Explainable AI: Mathematical Foundations for Trustworthy Models

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Hypercomplex Explainable AI: Mathematical Foundations for Trustworthy Models The HyperXAI project investigates the mathematical foundations of explainable artificial intelligence (XAI) for hypercomplex neural networks (HvNN). While the rapid expansion of artificial neural networks (ANNs), particularly deep learning, has produced major advances in fields such as image recognition and natural language processing, the reasoning behind their decisions often remains opaque. This challenge becomes even more critical when models deal with complex data structures such as colour information, multidimensional signals, or higher-order spatial relations. Hypercomplex neural networks—based on algebras such as complex numbers, quaternions, and Cayley–Dickson extensions—offer promising solutions, yet systematic approaches to their explainability are still at an early stage. HyperXAI addresses this gap by integrating advanced mathematics with physics-inspired methodologies to develop rigorous frameworks for explainability and trustworthiness in HvNN. The

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recordType

award

status

SIGNED

region

EU

value

871740

unit

EUR