Transparent, Reliable and Unbiased Smart Tool for AI

Informazioni relative al progetto

TRUST-AI

ID dell’accordo di sovvenzione: 952060

DOI

10.3030/952060

Progetto chiuso

Data della firma CE 4 Giugno 2020

Data di avvio 1 Ottobre 2020

Data di completamento 31 Marzo 2025

Finanziato da

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Costo totale

€ 3 996 418,75

Contributo UE

€ 3 996 418,75

Coordinato da

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

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (20)

Management Report

This deliverable concerns a status report on the technical achievements of TRUST in the first nine months of the projects A brief description of the development of each task will be provided including documentation of procedures screenshots preliminary results and identified risks

Data management plan V2
Final validation of the learned explainable AI models (online retail)

In this deliverable, a final validation of the proposed approach will be provided. The results and final conclusions of the online retail use case will be described together with lessons learned and recommendations for future developments.

Final validation of the learned explainable AI models for the energy use case

In this deliverable, a final validation of the proposed approach will be provided. The results and final conclusions of the energy use case will be described together with lessons learned and recommendations for future developments.

Saliency measures for identifying causally variables of explanations

This report will present the saliency measures and code for identifying causally relevant variables of humanlike explanations The relevant variables are those to be considered during the discovery and communication of causal explanations These variables will be formalised and measurable in terms of their specificity insensitivity proximity and other characteristics known to be preferred by humans

User studies on the realization of explanations

The deliverable reports the result of the qualitative studies on the best way to present explanation content produced in WP3 and provides recommendations for Task 22

Communication & Dissemination report

This deliverable will present a final status on the achievement of the project objectives in terms of communication and dissemination. The report will refer to the KPIs proposed on Deliverable D8.1.

Evaluation by human observers of different explainability formats

This deliverable reports the results of the evaluation of explainability performed in Task2.3.

Final validation of the learned explainable AI models (healthcare)

In this deliverable, a final validation of the proposed approach will be provided. The results and final conclusions of the healthcare use case will be described together with lessons learned and recommendations for future developments.

Data management plan V3
Dialog WP4-WP3

In this deliverable, the results of the interaction between WP4 and WP3 will be presented. The final outcome is the generation of explainable expressions by iterated dialog with the user in the proposed toy problems.

Exploitation Plan

This report described the future exploration of the framework, detailed in Task 8.3.Partners will consolidate all relevant findings, identify risks and evaluate the potential applicability of TRUST components in different sectors, covering many aspects of AI.

Communication & Dissemination update

This deliverable will present an updated status of the CDP, reporting the achievements obtained for each KPI and possible necessary changes to the plan.

Framework requirements document

This deliverable will describe the functional and nonfunctional requirements of the framework as well as the interactions and dependencies between the building blocks The use case needs will also be detailed and reported in this document ensuring that the framework is adequate to different problems and sectors

Communication & Dissemination plan

This report will present the Communication Dissemination Plan of TRUST where the strategy to raise public awareness about the project outcomes will be detailed and scheduled In addition to academic publications and conferences the plan includes events promotion participation in working groups and online forums and educational content creation such as courseware and webinars The plan will include KPIs and their target values as well as the Partner responsible for each communicationdissemination method

Evaluation with healthcare experts of learned models

This deliverable concerns a formal validation of the AI models developed for the first simplified version of the healthcare problem These models will be designed by NWOI and validated by medical experts from LUMC The report will present the first insights on the models results and suggestions for modifications

Initial validation of the explainable AI models from business experts

This deliverable concerns a formal validation of the AI models developed for the first simplified version of the online retail problem These models will be designed by LTP and validated by practitioners from Sonae INESC will coordinate the development and validation process

Data management plan

This deliverable presents the Data Management Plan of TRUSTAI detailing the types of data generatedcollected how it will be exploited protected and the standards to be considered

Framework validation

This deliverable consolidates the final conclusions from the use cases and describes the primary outcomes of TRUST framework. Recommendations for future extensions will also be included.

Initial validation of the explainable AI models from energy experts

This deliverable concerns a formal validation of the AI models developed for the first simplified version of the energy problem These models will be designed by POLIS21 and validated by practitioners from the industry

Other (2)

Automated medical image analysis learning techniques

This deliverable concerns the automated image analysis learning techniques integrated with TRUST blocks.

Project website

This deliverable will present the specification, organization and features of TRUST-AI website. The DNS, URL to access and screenshots on each page will also be presented.

Pubblicazioni

Conference proceedings (16)

Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression

Autori: M. Virgolin and P.A.N. Bosman
Pubblicato in: GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022, Pagina/e 2289–2297
Editore: ACM
DOI: 10.1145/3520304.3534036

Symbolic Pricing Policies for Attended Home Delivery - the Case of a European Retailer

Autori: Miguel Lunet, Daniela Fernandes, Fábio Neves-Moreira, Pedro Amorim
Pubblicato in: GECCO '25: Genetic and Evolutionary Computation Conference, 2025
Editore: Digital Library

Multi-objective Genetic Programming for Explainable Reinforcement Learning

Autori: Videau, Mathurin; Ferreira Leite, Alessandro; Teytaud, Olivier; Schoenauer, Marc
Pubblicato in: EUROGP - 25th European Conference on Genetic Programming, part of EvoStar 2022, Numero 25, 2022, Pagina/e pp.278-293, ISBN 978-3-031-02055-1
Editore: Springer Verlag
DOI: 10.1007/978-3-031-02056-8_18

Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis

Autori: Eduard Barbu, Marharytha Domnich, Raul Vicente, Nikos Sakkas, André Morim
Pubblicato in: The 2nd World Conference on eXplainable Artificial Intelligence (xAI 2024), July 17-19, 2024 - Valletta, Malta, 2024
Editore: Springer
DOI: 10.48550/arxiv.2405.11958

Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models

Autori: Sijben, Evi; Alderliesten, Tanja; Bosman, Peter
Pubblicato in: GECCO '22: Genetic and Evolutionary Computation Conference, 2022, ISBN 978-1-4503-9237-2
Editore: Association for Computing Machinery, New York, NY, United States
DOI: 10.48550/arxiv.2203.13347

Memetic Semantic Genetic Programming for Symbolic Regression

Autori: Alessandro Leite and Marc Schoenauer
Pubblicato in: 26th EuroGP - Part of EvoStar 2023, Numero 26, 2023, Pagina/e 198–212, ISBN 978-3-031-29572-0
Editore: Springer Verlag LNCS-13986
DOI: 10.1007/978-3-031-29573-7_13

A Guide for Practical Use of ADMG Causal Data Augmentation

Autori: Poinsot, Audrey; Leite, Alessandro
Pubblicato in: Workshop on the pitfalls of limited data and computation for Trustworthy ML, ICLR 2023, 2023
Editore: OpenReview
DOI: 10.48550/arxiv.2304.01237

Explanatory World Models via Look Ahead Attention for Credit Assignment

Autori: Oriol Corcoll and Raul Vicente
Pubblicato in: Numero 26403498, 2022, ISSN 2640-3498
Editore: Proceedings of Machine Learning Research

Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals

Autori: Evi Sijben, Jeroen Jansen, Peter Bosman, Tanja Alderliesten
Pubblicato in: Proceedings of the Genetic and Evolutionary Computation Conference, 2024, Pagina/e 1354-1362
Editore: ACM
DOI: 10.1145/3638529.3654145

Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning

Autori: Labash, Aqeel; Fletzer, Florian; Majoral, Daniel; Vicente, Raul
Pubblicato in: ICML'23: Proceedings of the 40th International Conference on Machine Learning, Numero 18, 2023
Editore: JMLR.org
DOI: 10.48550/arxiv.2307.12143

COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images

Autori: Dmytro Shvetsov, Joonas Ariva, Marharyta Domnich, Raul Vicente, Dmytro Fishman
Pubblicato in: The 2nd World Conference on eXplainable Artificial Intelligence (xAI 2024), July 17-19, 2024 - Valletta, Malta, 2024
Editore: Springer
DOI: 10.48550/arxiv.2404.12832

Learning symbolic expressions to solve multi-period time slot pricing vehicle routing problems

Autori: Fábio Neves-Moreira, Daniela Fernandes, Miguel Lunet, Pedro Amorim
Pubblicato in: IJCAI 2024 - International Joint Conference on Artificial Intelligence, Jeju, South Korea, 2024
Editore: IJCAI

Deep learning-based auto-segmentation of paraganglioma for growth monitoring

Autori: E.M.C. Sijben, J.C. Jansen, P.A.N. Bosman (Peter), and T. Alderliesten
Pubblicato in: Proceedings Volume 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment, 2024, Pagina/e 1292916
Editore: SPIE
DOI: 10.1117/12.3006413

Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence

Autori: Marharyta Domnich, Raul Vicente
Pubblicato in: The 2nd World Conference on eXplainable Artificial Intelligence (xAI 2024), July 17-19, 2024 - Valletta, Malta, 2024
Editore: Springer
DOI: 10.48550/arxiv.2404.12810

Real time Data and Application Sharing and Collaboration for the Building Energy Domain

Autori: N. Sakkas, M. Papadopoulou, D. Sakkas
Pubblicato in: WDBE 2021, 2021
Editore: World of Digital Built Environment WDBE 2021

Evolvability degeneration in multi-objective genetic programming for symbolic regression

Autori: Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
Pubblicato in: GECCO '22: Genetic and Evolutionary Computation Conference, 2022
Editore: Association for Computing Machinery, New York, NY, United States
DOI: 10.1145/3512290.3528787

Peer reviewed articles (17)

Improving asset management in capital-intensive industries: Case study of a Portuguese water utility

Autori: Mariana Casalta, Flávia Barbosa, Luciana Yamada, Lígia B. Ramos
Pubblicato in: Utilities Policy, Numero 91, 2024, Pagina/e 101822, ISSN 0957-1787
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.jup.2024.101822

Mind the gap: challenges of deep learning approaches to Theory of Mind

Autori: Aru, Jaan; Labash, Aqeel; Corcoll, Oriol; Vicente, Raul
Pubblicato in: Artificial Ingtelligence Review, Numero 3, 2023, ISSN 0269-2821
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10462-023-10401-x

Learning efficient in-store picking strategies to reduce customer encounters in omnichannel retail

Autori: Fábio Neves-Moreira, Pedro Amorim
Pubblicato in: International Journal of Production Economics, 2024, ISSN 0925-5273
Editore: Elsevier BV
DOI: 10.1016/j.ijpe.2023.109074

Scheduling wagons to unload in bulk cargo ports with uncertain processing times

Autori: Cristiane Ferreira, Gonçalo Figueira, Pedro Amorim, Alexandre Pigatti
Pubblicato in: Computers & Operations Research, 2023, ISSN 0305-0548
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.cor.2023.106364

Open data or open access? The case of building data.

Autori: Sakkas, N., Yfanti, S
Pubblicato in: Academia Letters, 2021, ISSN 2771-9359
Editore: Academia.edu
DOI: 10.20935/al3629

Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments

Autori: Marharyta Domnich, Julius Välja, Rasmus Moorits Veski, Giacomo Magnifico, Kadi Tulver, Eduard Barbu, Raul Vicente
Pubblicato in: Proceedings of the AAAI Conference on Artificial Intelligence, Numero 39, 2025, Pagina/e 16308-16316, ISSN 2374-3468
Editore: Association for the Advancement of Artificial Intelligence (AAAI)
DOI: 10.1609/aaai.v39i15.33791

Memetic semantic boosting for symbolic regression

Autori: Alessandro Leite, Marc Schoenauer
Pubblicato in: Genetic Programming and Evolvable Machines, Numero 26, 2025, ISSN 1389-2576
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10710-024-09506-1

Explainable Approaches for Forecasting Building Electricity Consumption

Autori: Nikos Sakkas, Sofia Yfanti,Pooja Shah, Nikitas Sakkas, Christina Chaniotakis, Costas Daskalakis, Eduard Barbu and Marharyta Domnich
Pubblicato in: Energies, 2023, ISSN 1996-1073
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/en16207210

Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

Autori: Stelzer, Florian; Röhm, André; Vicente, Raul; Fischer, Ingo; Yanchuk, Serhiy
Pubblicato in: Nature Communications, Numero 20411723, 2021, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.48550/arxiv.2011.10115

Technology Readiness Levels (TRLs) in the Era of Co-Creation

Autori: Sofia Yfanti, Nikos Sakkas
Pubblicato in: Applied System Innovation, 2024, ISSN 2571-5577
Editore: MDPI
DOI: 10.3390/asi7020032

A cooperative coevolutionary hyper-heuristic approach to solve lot-sizing and job shop scheduling problems using genetic programming

Autori: Yannik Zeiträg, José Rui Figueira, Gonçalo Figueira
Pubblicato in: International Journal of Production Research, 2024, ISSN 0925-5273
Editore: Elsevier BV
DOI: 10.1080/00207543.2023.2301044

Building data models and data sharing. Purpose, approaches, and a case study on explainable demand response

Autori: Nikos Sakkas, Christina Chaniotaki and Nikitas Sakkas
Pubblicato in: IOP Conference Series: Earth and Environmental Science, 2023, ISSN 1757-899X
Editore: IOP Science
DOI: 10.1088/1755-1315/1122/1/012066

Personalized choice model for forecasting demand under pricing scenarios with observational data—The case of attended home delivery

Autori: Özden Gür Ali, Pedro Amorim
Pubblicato in: International Journal of Forecasting, 2024, Pagina/e 706-720, ISSN 0169-2070
Editore: Elsevier BV
DOI: 10.1016/j.ijforecast.2023.04.008

Quantifying Reinforcement-Learning Agent’s Autonomy, Reliance on Memory and Internalisation of the Environment

Autori: Anti Ingel, Abdullah Makkeh, Oriol Corcoll and Raul Vicente
Pubblicato in: Entropy, Numero 10994300, 2022, ISSN 1099-4300
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/e24030401

Interpretable Forecasting of Energy Demand in the Residential Sector

Autori: Nikos Sakkas; Sofia Yfanti; Costas Daskalakis; Eduard Barbu; Marharyta Domnich
Pubblicato in: Energies, Numero 1, 2021, ISSN 1996-1073
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/en14206568

Do Deep Reinforcement Learning Agents Model Intentions?

Autori: Tambet Matiisen; Aqeel Labash; Daniel Majoral; Jaan Aru; Raul Vicente
Pubblicato in: Stats, Vol 6, Iss 1, Pp 50-66 (2022), Numero 5, 2022, ISSN 2571-905X
Editore: MDPI
DOI: 10.3390/stats6010004

Drivers of and counterfactuals for the final energy and electricity consumption in EU industry

Autori: Sakkas, N., Athanasiou, N.
Pubblicato in: Academia Letters, Numero 27719359, 2021, ISSN 2771-9359
Editore: Academia.edu
DOI: 10.20935/al3451

Thesis and dissertations (4)

Genetic Programming Approaches for Solving Transportation Problems

Autori: Catarina Furtado Martins da Rocha Leite
Pubblicato in: 2022
Editore: University of Porto

Using Dimensionally Aware Genetic Programming to find interpretable Dispatching Rules for the Job Shop Scheduling Problem

Autori: Álvaro Manuel Festas Pereira da Silva
Pubblicato in: 2021
Editore: University of Porto

A generic scalable web platform for XAI algorithms

Autori: Luís Pedro Viana Ramos
Pubblicato in: 2022
Editore: University of Porto

Interface Design for Human-guided Explainable AI

Autori: João Rafael Gomes Varela
Pubblicato in: 2022
Editore: University of Porto

Book chapters (1)

Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression

Autori: Johannes Koch, Tanja Alderliesten, Peter A. N. Bosman
Pubblicato in: Lecture Notes in Computer Science, Parallel Problem Solving from Nature – PPSN XVIII, 2024, Pagina/e 238-255
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-70055-2_15