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TRUstworthy huMAN-centric artificial intelligence

Project description

Building secure and fair AI systems for industry and beyond

In recent years, advances in AI have benefited most industries, driving key insights and breakthroughs across sectors such as healthcare, transportation, finance, and manufacturing. The EU-funded TRUMAN project aims to develop generic methodologies and technologies that enhance AI system resilience against privacy, fairness, and security threats, while also increasing user trust. To achieve this, the research will focus on large language models, continual learning, and knowledge graph learning and representation, developing solutions tailored to diverse scenarios.

Objective

Artificial Intelligence (AI) has become the main driver of growth in information technology, touching all sectors in the industry, ranging from healthcare, to finance, and transport. The goal of TRUMAN is to design and develop generic technologies and methodologies for improving AI systems’ resilience against security, privacy, and fairness attacks, as well as to increase the trust that their users have in these systems, while accounting for different phases of the AI life cycle, starting from data collection through training and deployment. TRUMAN will encompass three major AI architectures: knowledge graph (KG) representation and learning, continual learning, and large language models (LLMs). These three approaches will be investigated with the goal of designing solutions aimed for scenarios involving dynamic data collection, distributed model training, and scenarios that involve human-in-the-loop (HITL) design principles. TRUMAN will develop customized robustness solutions for both existing and newly developed privacy, adversarial, and fairness attacks. The project will also consider the impact of these solutions on humans, how to explain to them the underlying technologies and the risks, and employ their help in the improvement of these models. These technologies will be evaluated against four use cases illustrating scenarios from different sectors, i.e. marketing, IT, finance, and healthcare. Moreover, the TRUMAN project will strive to generate generic/holistic guidelines and recommendations for building trustworthy and robust AI systems.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-CL4-2024-HUMAN-03

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Coordinator

EURECOM GIE
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.

€ 706 910,00
Address
ROUTE DES CHAPPES 450 CAMPUS SOPHIA ANTIPOLIS
06410 BIOT
France

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Region
Provence-Alpes-Côte d’Azur Provence-Alpes-Côte d’Azur Alpes-Maritimes
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.

€ 706 910,00

Participants (10)

Partners (1)

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