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H2020Doctoral network2019–2024

NL4XAI · Interactive Natural Language Technology for Explainable Artificial Intelligence

Horizon 2020 — Marie Skłodowska-Curie Actions

Duration
2019-10-01 → 2024-09-30
EU contribution
€2,843,888
Participants
11
Scheme
MSCA-ITN

Lines connect the coordinator with its partners.

Results in brief

Interactive Natural Language Technology for Explainable Artificial Intelligence

This is the first European Training Network (ETN) on Natural Language (NL) and Explainable AI (XAI). This four-year European project brings together 21 institutions, including 13 academic institutions and 8 private companies, creating a vibrant ecosystem for collaboration. The main goal is to train 11 creative, entrepreneurial, and innovative early-stage researchers (ESRs) who face the challenge of designing and implementing self-explanatory AI systems. ESRs profit from a broad program of training events and opportunities, ranging from network-wide events to courses covering technical and scientific domains and transferable skills. Each ESR works on an individual research project at one of the network’s host organizations and takes part in network-wide training events and meetings, as well as in secondments to other beneficiaries or partners. As a result, ESRs are prepared to design and build transparent and trustworthy AI systems that generate interactive explanations on the basis of NL and visual tools, which are intuitively understandable by everyone, even by non-expert users, validated by humans in specific use cases, and accessible to all European citizens. Namely, four ESRs design and develop XAI models; two ESRs enhance NL technology for XAI; two ESRs exploit argumentation technology for XAI; and three ESRs develop interactive interfaces for XAI. It is worth noting that the development of NL4XAI systems addresses technical issues (i.e., designing explainable algorithms and human-machine interfaces) as well as ethical, legal, socio-economic, and cultural issues. Moreover, their validation complies with the EU General Data Protection Regulation (GDPR) and the EU regulation for AI (AI Act) and agrees with the 7 requirements (Human Agency and Oversight; Technical Robustness and Safety; Privacy and Data Governance; Transparency; Diversity, Non-discrimination and Fairness; Societal and Environmental Well-being; Accountability) included in the EU Ethics Guidelines and Assessment List for Trustworthy AI.

Data: CORDIS, © European Union

Project objective

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.

Original text from CORDIS.

Participants

  • UNIVERSIDAD DE SANTIAGO DE COMPOSTELA · Santiago De CompostelaCoordinatorSpain
  • AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS · MadridSpain
  • CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS · ParisFrance
  • INDRA SOLUCIONES TECNOLOGIAS DE LA INFORMACION SL · ALCOBENDAS - MADRIDSpain
  • INSTYTUT FILOZOFII I SOCJOLOGII POLSKIEJ AKADEMII NAUK · WARSZAWAPoland
  • POLITECHNIKA WARSZAWSKA · WarszawaPoland
  • TECHNISCHE UNIVERSITEIT DELFT · DelftNetherlands
  • THE UNIVERSITY COURT OF THE UNIVERSITY OF ABERDEEN · AberdeenUnited Kingdom
  • UNIVERSITA TA MALTA · MSIDAMalta
  • UNIVERSITEIT TWENTE · EnschedeNetherlands
  • UNIVERSITEIT UTRECHT · UtrechtNetherlands

Links

Data: CORDIS, © European Union