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TRUST-AI

Financiado

Transparent Reliable and Unbiased Smart Tool for AI

Artificial intelligence is single-handedly changing decision-making at different levels and sectors in often unpredictable and uncontrolled ways. Due to their black-box nature, existing models are difficult to interpret, and hence... ver más

31/03/2025

INESC TEC

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

INESC TEC INSTITUTO DE ENGENHARIADE SISTEMAS... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 8 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-06-04

FETPROACT-EIC-05-2019: FET Proactive: emerging paradigms a...

I+D

Cerrada hace 5 años

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8 Participantes

INESC TEC

898.75K€ | Lider

JUNCA GELATINES

232.70K€ | Participante

NWO-I

698.80K€ | Participante

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Duración del proyecto: 57 meses Fecha Inicio: 2020-06-04
Fecha Fin: 2025-03-31

Líder del proyecto

INESC TEC INSTITUTO DE ENGENHARIADE SISTEMAS... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Descripción del proyecto Artificial intelligence is single-handedly changing decision-making at different levels and sectors in often unpredictable and uncontrolled ways. Due to their black-box nature, existing models are difficult to interpret, and hence trust. Explainable AI is an emergent field, but, to ensure no loss of predictive power, many of the proposed approaches just build local explanators on top of powerful black-box models. To change this paradigm and create an equally powerful, yet fully explainable model, we need to be able to learn its structure. However, searching for both structure and parameters is extremely challenging. Moreover, there is the risk that the necessary variables and operators are not provided to the algorithm, which leads to more complex and less general models. It is clear that state-of-the-art, yet practical, real-world solutions cannot come only from the computer science world. Our approach therefore consists in involving human intelligence in the discovery process, resulting in AI and humans working in concert to find better solutions (i.e. models that are effective, comprehensible and generalisable). This is made possible by employing ‘explainable-by-design’ symbolic models and learning algorithms, and by adopting a human-centric, ‘guided empirical’ learning process that integrates cognition, machine learning and human-machine interaction, ultimately resulting in a Transparent, Reliable and Unbiased Smart Tool. This proposal aims to design TRUST, ensure its adequacy to tackle predictive and prescriptive problems, and create an innovation ecosystem around it, whereby academia and companies can further exploit it, independently or in collaboration. The proposed ‘human-guided symbolic learning’ should be the next ‘go-to paradigm’ for a wide range of sectors, where human agency / accountability is essential. These include healthcare, retail, energy, banking, insurance and public administration (of which the first three are explored in this project).

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