Projet NL4XAI (Horizon 2020, appel à projets 2019)
Action : Doctoral Networks
Panel : Information Science and Engineering
Statut du projet : SIGNED

According to Polanyi's paradox, humans know more than they can explain, mainly due to the huge amount of implicit knowledge they unconsciously acquire trough culture, heritage, etc. The same applies for Artificial Intelligence (AI) systems mainly learnt automatically from data. However, in accordance with EU laws, humans have a right to explanation of decisions affecting them, no matter who (or what AI system) makes such decision. NL4XAI will train 11 creative, entrepreneurial and innovative early-stage researchers (ESRs), who will face the challenge of making AI self-explanatory and thus contributing to translate knowledge into products and services for economic and social benefit, with the support of Explainable AI (XAI) systems. Moreover, the focus of NL4XAI is in the automatic generation of interactive explanations in natural language (NL), as humans naturally do, and as a complement to visualization tools. As a result, ESRs are expected to leverage the usage of AI models and techniques even by non-expert users. Namely, all their developments will be validated by humans in specific use cases, and main outcomes publicly reported and integrated into a common open source software framework for XAI that will be accessible to all the European citizens. In addition, those results to be exploited commercially will be protected through licenses or patents. It is worthy to note that we have selected some of the most prominent European researchers (from both academy and industry) in each of the related fundamental topics and created a joint high quality training program that can be seen as a pyramid with the main research objective (designing and building XAI models) on top. It will be achieved as result of jointly addressing the research objectives in the pyramid base (NL generation and processing for XAI; Argumentation Technology for XAI; Interactive Interfaces for XAI). ESRs are also to be trained in Ethical and Legal issues, as well as in transversal skills.

Mots clés : explainable ai, natural language generation, argumentation technology, computational intelligence, soft computing, computing with words, interpretable machine learning

Participants :
- Spanish National Research Council (Espagne) — Partner, beneficiary, Recherche, subvention : 257874.46 €
- Polish Academy of Sciences (Pologne) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 0.0 €
- University of Malta (Malte) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 237955.68 €
- Utrecht University (Pays-Bas) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 265619.88 €
- University of Aberdeen (Royaume-Uni) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 303172.56 €
- Delft University of Technology (Pays-Bas) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 265619.88 €
- University of Santiago de Compostela (Espagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 494840.18 €
- Indra soluciones tecnologias de la informacion sl (Espagne) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 250904.88 €
- Centre national de la recherche scientifique (France) — Partner, beneficiary, Recherche, subvention : 274802.04 €
- University of Twente (Pays-Bas) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 265619.88 €
- Warsaw University of Technology (Pologne) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 227478.6 €