Project description
Making it easier for machines to explain their decisions
Under European Union regulations, all citizens have the right to information and explanation on any decision that affects them. This includes the decisions made by artificial intelligence (AI) systems. This means computer systems should work as expected and produce transparent explanations and reasons for the decisions they make. This is called Explainable AI (XAI). The EU-funded NL4XAI project will train 11 early-stage researchers to create a supportive system for AI (XAI), enabling it to translate its knowledge into a natural language. The project will draw on the potentials of natural language generation and processing, argumentation technology and interactive technology for XAI. The outcome will be tested and results added to the open-source framework for XAI.
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.
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Coordinator
15782 Santiago De Compostela
Spain