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Transparent, Reliable and Unbiased Smart Tool for AI

Project description

DEENESFRITPL

Building solutions together with artificial intelligence

Due to their black-box nature, existing artificial intelligence (AI) models are difficult to interpret, and hence trust. Practical, real-world solutions to this issue cannot come only from the computer science world. The EU-funded TRUST-AI project is involving human intelligence in the discovery process. It will employ 'explainable-by-design' symbolic models and learning algorithms and adopt a human-centric, 'guided empirical' learning process that integrates cognition. The project will design TRUST, a trustworthy and collaborative AI platform, ensure its adequacy to tackle predictive and prescriptive problems and create an innovation ecosystem in which academics and companies can work independently or together.

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).

Fields of science

Programme(s)

Topic(s)

Call for proposal

H2020-FETPROACT-2019-2020

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Sub call

H2020-EIC-FETPROACT-2019

Funding Scheme

RIA - Research and Innovation action

Coordinator

INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA
Net EU contribution
€ 898 750,00
Address
Rua dr roberto frias campus da feup
4200 465 Porto
Portugal

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Region
Continente Norte Área Metropolitana do Porto
Activity type
Research Organisations
Links
Other funding
€ 0,00

Participants (6)

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EC signature date 4 June 2020
Last update: 25 November 2022

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Permalink: https://cordis.europa.eu/project/id/952060

European Union, 2023