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Hypercomplex Explainable AI: Mathematical Foundations for Trustworthy Models

Objective

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 project will design novel XAI methods tailored to hypercomplex architectures, validate them on synthetic and publicly available benchmark datasets across multiple data modalities, and propose a unified framework for HvNN interpretability.

By embedding explainability directly into HvNN models, HyperXAI will enhance transparency, reproducibility, and reliability. Beyond methodological advances, the project will build a sustainable international network of researchers from Poland, Spain, Czechia, Latvia, Azerbaijan, Türkiye, Iraq, South Korea, and Brazil, representing diverse traditions in mathematics, physics, and computer science.

Through staff exchanges, joint research, training schools, and open science practices, HyperXAI will strengthen interdisciplinary expertise and foster the development of a new generation of researchers. By consolidating the theoretical and computational basis of explainability for HvNN, the project will stimulate the adoption of hypercomplex XAI within the global scientific comm

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-SE - HORIZON TMA MSCA Staff Exchanges

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Call for proposal

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(opens in new window) HORIZON-MSCA-2025-SE-01

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Coordinator

POLITECHNIKA KRAKOWSKA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 220 440,00
Address
WARSZAWSKA 24
31-155 KRAKOW
Poland

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Region
Makroregion południowy Małopolskie Miasto Kraków
Activity type
Higher or Secondary Education Establishments
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Participants (7)

Partners (4)

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