1. Present the potential of AI for SDGs

Starting with 2 case studies in education and health: defining sustainable processes and structures (governance, access, business models, licensing, etc.) as well as developing a suitable software infrastructure (APIs and tools to aggregate existing tools and algorithms and to make them easily deployable in applications, as well as to access data and computing resources). Collect data from available data sources to create an infrastructure to ingest, process, analyze, aggregate and enrich specific-domain data, for specific SDG challenges. This infrastructure will scale to large amounts of data, starting from the education based project (in the millions of OER audio, video and animation, curricula, syllabi, lecture notes, assignments, tests, projects, courses, course materials, modules, textbooks, tests and datasets) that will form the basis for the algorithms to mine, represent, reason upon and use this diversity of information. This would be society agnostic, but would include the countries which are already OER adopters. As argued before, the core partnership lies with research institutes and partially universities. They are in the countries listed above. Not all countries, however, are represented in our network. While they still can be included in individual projects or as object of study, and certainly in the dissemination activities. In addition, it will also provide a test bed for other researchers outside the AI domain who might be interested in accessing the data processed and produced in the project. Access to the data sets and its metadata will be provided via a Web-based API, which will furthermore allow publishing new data sources.

  1. Place the issue of AI for SDGs on the national, regional and international agenda

To identify options to harness the potential of rapid technological change and innovation towards achieving the Sustainable Development Goals with the use of AI. Collect information on, gain insight in, and identify the major characteristics (both similarities and distinctions) of AI developments at the national (and regional) level in a series of contexts that may be considered representative for the full spectrum of data science as well as for the variety in societies (e.g., The Netherlands, UK, Spain, Turkey, India, China, South Africa, Nigeria, Brazil, USA, Canada, Australia, New Zealand, and Commonwealth states). The core partnership for this objective lies with already established connections with Computer Science departments and Data partners,

  1. Mobilizing an AI community

Including researchers, businesses and start-ups to provide access to knowledge, algorithms and tools for achieving SDGs. The increasing capability and use of machine learning, the rising creation of augmented reality content, and the changing capabilities and uses of smartphones have broad potential to contribute towards the SDGs.

  1. Evaluate the critical success and failure factors

Among the national/regional AI case studies in relation to the variety of contexts, convert these into a context dependent multi-facetted framework of best conditions and guidelines for implementing an AI strategy at the national (or regional) level, and derive a set of AI scenarios fit for specific contexts. Analyze the educational, economic and societal impact of the national / regional AI case studies, advise on new requirements for machine learning and educational environments, and develop context dependent business models, explicitly taking into account societal benefits.

  1. Disseminate and share the broad AI knowledge

That has been created and derived in the project, more specifically provide good and bad practices, underline the contextual dependence, and give guidance and basic support to new national (or regional) AI initiatives. With instruments as visiting professorships, joint research projects, scholarships for PhD students the project will provide opportunities and support to the capacity building of partners in different regions.