The TAILOR consortium is committed to demonstrate that research and innovation based on expertise, coopetition, and diversity can achieve the European vision of Human-Centered Trustworthy AI and make Europe the global role-model for responsible AI.
Objective 1: Establish a strong pan-European network of research excellence centres on the Foundations of Trustworthy AI
The consortium will build a fully functional network of centres of research excellence on the foundations of Trustworthy AI covering all of Europe. The project will define the governance structure of the network, leveraging existing expertise and platforms ecosystems. The strategic, organisational and procedural aspects will be defined together with the more operational and functional aspects. In the end, this structure will give rise to a culture of sustained collaborations and transformative innovations. The AI-on-demand platform will be the backbone of the network.
Objective 2: Define and maintain a unified strategic research and innovation roadmap for the Foundations of Trustworthy AI
The network will define a roadmap on the foundations of Trustworthy AI for the years 2020-2030, which will be pushed and continuously tracked by the network. Seeded with existing Roadmaps/Agendas worldwide, the TAILOR roadmap will be the result of a collaborative effort between TAILOR participants in at least 21 countries, strongly focusing on AI research that can be either curiosity-driven or application-driven. For curiosity-driven research the roadmap will target grand challenges with long-term impact to ensure excellent research and help training the best AI talents in Europe. For application-driven research the future avenues will be identified by combining extensive requirements collection from vertical domains, with horizontal cross-pollination and leverage, and its transfer to industry. In both cases, the TAILOR roadmap will be coordinated with the CSA and the other networks and actively pursue alignment with European Commission Work Programmes and EU-wide relevant actors such as the High-Level Expert Group on AI, AI Watch, EurAI, AI4EU, AI Alliance, and PPPs.
Objective 3: Create the capacity and critical mass to develop the scientific foundations for Trustworthy AI
The TAILOR project brings together leading AI research centres as partners as well as network members from learning, optimisation and reasoning together with internationally recognized European companies representing important industry sectors into a single scientific network guided by a strategic research and innovation roadmap thereby reducing the fragmentation, boosting the collaboration, increase impact of funding, and increasing the AI research capacity of Europe as well as developing, attracting and retaining talent in Europe. The objective is for the network to enable faster research progress by providing a common resource for Europe and the world with easy access to state-of-the-art knowledge and expertise in the foundations of Trustworthy AI. The TAILOR network actively promotes and funds collaborative projects and extended research visits both among TAILOR partners and the network members through a connectivity fund. These are aimed at achieving effective collaboration and foster mobility and knowledge transfer among European AI research centers. By creating an exciting and stimulating research environment with the best researchers in the world we expect AI researchers to stay in Europe after completing their PhDs.
Objective 4: Progress the Scientific State-of-the-Art for the Foundations of Trustworthy AI
The overall scientific objective of TAILOR is to develop the scientific foundations for trustworthy AI. Trustworthy AI is high on both the political and scientific agenda for AI (cf. the report of the High-Level Expert Group on AI), and it is considered one of the hallmarks of AI made in Europe. Developing trustworthy AI requires all of artificial intelligence to work in sync. The technological foundations for AI are learning, reasoning and optimisation, but they do not work seamlessly together, as they were developed independently. Therefore, realizing trustworthy AI is not possible without tightly integrating learning, reasoning and optimisation.
Objective 5: Build sustained collaborations with academic, industrial, governmental, and community stakeholders on the Foundations of Trustworthy AI
The TAILOR project will develop an Innovation and Transfer Program to develop synergies and cross-fertilization between industry and the TAILOR network as well as foster innovation and exploit new ideas.. The program will organize multi-stakeholder theme development workshops to define the strategic private and public sector challenges and the research necessary to address these. The identified strategic challenges will then be further refined and embodied in uses cases, competitions, challenges, and benchmarks. TAILOR will support industry internships and develop a common academic/industrial PhD programmes and post-PhD programmes with a focus on industrial challenges. The numbers in the table below indicate the goals to be reached at that point in time.
Objective 6: Increase Knowledge and Awareness of the Foundations of Trustworthy AI across Europe
To maximize the impact and awareness of the TAILOR project, well-orchestrated dissemination and communication activities are necessary. The activities will be aimed to spread the knowledge generated in the project to become a reference point for researchers on Trustworthy AI in Europe, for connecting and preparing the exploitation of this knowledge at an industrial level through tight collaboration with Digital Innovation Hubs, and strengthen the AI-on-Demand platform with assets generated in the project. TAILOR will identify the most relevant stakeholder groups (WHO) and which results to disseminate and communicate to each group (WHAT), develop detailed plans for communication, dissemination and exploitation with concrete actions (HOW), timeline (WHEN), and assign responsibilities to specific partners for implementing these actions (BY WHOM). These activities will be continuously monitored to assess their effectiveness.