CORDIS
EU research results

CORDIS

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Transparent, Reliable and Unbiased Smart Tool for AI

Project information

Grant agreement ID: 952060

Status

Grant agreement signed

  • Start date

    1 October 2020

  • End date

    30 September 2024

Funded under:

H2020-EU.1.2.2.

  • Overall budget:

    € 3 996 418,75

  • EU contribution

    € 3 996 418,75

Coordinated by:

INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA

Portugal

Objective

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).

Coordinator

INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA

Address

Rua Dr Roberto Frias Campus Da Feup
4200 465 Porto

Portugal

Activity type

Research Organisations

EU Contribution

€ 898 750

Participants (6)

TARTU ULIKOOL

Estonia

EU Contribution

€ 600 625

INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE

France

EU Contribution

€ 476 373,75

STICHTING NEDERLANDSE WETENSCHAPPELIJK ONDERZOEK INSTITUTEN

Netherlands

EU Contribution

€ 698 795

APPLIED INDUSTRIAL TECHNOLOGIES (APINTECH)

Cyprus

EU Contribution

€ 422 500

LTPLABS LDA

Portugal

EU Contribution

€ 298 750

TAZI BILISIM TEKNOLOJILERI ANONIM SIRKETI

Turkey

EU Contribution

€ 600 625

Project information

Grant agreement ID: 952060

Status

Grant agreement signed

  • Start date

    1 October 2020

  • End date

    30 September 2024

Funded under:

H2020-EU.1.2.2.

  • Overall budget:

    € 3 996 418,75

  • EU contribution

    € 3 996 418,75

Coordinated by:

INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA

Portugal