Deliverables
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 V2Saliency 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 explanationsThe 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
Framework requirements documentThis 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 planThis 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 modelsThis 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 expertsThis 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 planThis 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
Initial validation of the explainable AI models from energy expertsThis 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
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.
Publications
Author(s):
Videau, Mathurin; Ferreira Leite, Alessandro; Teytaud, Olivier; Schoenauer, Marc
Published in:
EUROGP - 25th European Conference on Genetic Programming, part of EvoStar 2022, Issue 25, 2022, Page(s) pp.278-293, ISBN 978-3-031-02055-1
Publisher:
Springer Verlag
DOI:
10.1007/978-3-031-02056-8_18
Author(s):
Nikos Sakkas, Ch. Chaniotaki, Nikitas. Sakkas, Costas Daskalakis
Published in:
Emerging Concepts for Sustainable Built Environment, 2022
Publisher:
SBEfin 2022 Conference
Author(s):
Sijben, Evi; Alderliesten, Tanja; Bosman, Peter
Published in:
GECCO '22: Genetic and Evolutionary Computation Conference, 2022, ISBN 978-1-4503-9237-2
Publisher:
Association for Computing Machinery, New York, NY, United States
DOI:
10.48550/arxiv.2203.13347
Author(s):
Alessandro Leite and Marc Schoenauer
Published in:
26th EuroGP - Part of EvoStar 2023, Issue 26, 2023, Page(s) 198–212, ISBN 978-3-031-29572-0
Publisher:
Springer Verlag LNCS-13986
DOI:
10.1007/978-3-031-29573-7_13
Author(s):
Oriol Corcoll and Raul Vicente
Published in:
Issue 26403498, 2022, ISSN 2640-3498
Publisher:
Proceedings of Machine Learning Research
Author(s):
Labash, Aqeel; Fletzer, Florian; Majoral, Daniel; Vicente, Raul
Published in:
ICML'23: Proceedings of the 40th International Conference on Machine Learning, Issue 18, 2023
Publisher:
JMLR.org
DOI:
10.48550/arxiv.2307.12143
Author(s):
N. Sakkas, M. Papadopoulou, D. Sakkas
Published in:
WDBE 2021, 2021
Publisher:
World of Digital Built Environment WDBE 2021
Author(s):
Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
Published in:
GECCO '22: Genetic and Evolutionary Computation Conference, 2022
Publisher:
Association for Computing Machinery, New York, NY, United States
DOI:
10.1145/3512290.3528787
Author(s):
Aru, Jaan; Labash, Aqeel; Corcoll, Oriol; Vicente, Raul
Published in:
Artificial Ingtelligence Review, Issue 3, 2023, ISSN 0269-2821
Publisher:
Kluwer Academic Publishers
DOI:
10.1007/s10462-023-10401-x
Author(s):
Sakkas, N., Yfanti, S
Published in:
Academia Letters, 2021, ISSN 2771-9359
Publisher:
Academia.edu
DOI:
10.20935/al3629
Author(s):
Stelzer, Florian; Röhm, André; Vicente, Raul; Fischer, Ingo; Yanchuk, Serhiy
Published in:
Nature Communications, Issue 20411723, 2021, ISSN 2041-1723
Publisher:
Nature Publishing Group
DOI:
10.48550/arxiv.2011.10115
Author(s):
Anti Ingel, Abdullah Makkeh, Oriol Corcoll and Raul Vicente
Published in:
Entropy, Issue 10994300, 2022, ISSN 1099-4300
Publisher:
Multidisciplinary Digital Publishing Institute (MDPI)
DOI:
10.3390/e24030401
Author(s):
Nikos Sakkas; Sofia Yfanti; Costas Daskalakis; Eduard Barbu; Marharyta Domnich
Published in:
Energies, Issue 1, 2021, ISSN 1996-1073
Publisher:
Multidisciplinary Digital Publishing Institute (MDPI)
DOI:
10.3390/en14206568
Author(s):
Tambet Matiisen; Aqeel Labash; Daniel Majoral; Jaan Aru; Raul Vicente
Published in:
Stats, Vol 6, Iss 1, Pp 50-66 (2022), Issue 5, 2022, ISSN 2571-905X
Publisher:
MDPI
DOI:
10.3390/stats6010004
Author(s):
Sakkas, N., Athanasiou, N.
Published in:
Academia Letters, Issue 27719359, 2021, ISSN 2771-9359
Publisher:
Academia.edu
DOI:
10.20935/al3451
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