Building blocks

The NL AIC works together on the necessary knowledge and expertise, resulting in five themes, also called building blocks. Those are important for a robust impact in economic and social sectors.
All building blocks:
    The NL AIC stimulates the development of AI education within all sectors in the Netherlands.


    Frans van Ette

    Chair working group Data Sharing

    Related posts

    Contribute to the development of AI in the Netherlands?

    Then join the NL AIC.

    Data Sharing

    Without data, no machine learning is possible. The more available relevant data, the better the predictions, thus improving the usefulness of the machine learning applications. Access to data is, therefore, crucial. In the Netherlands, data is often kept locked, usually for legal or commercial reasons. To break through these existing barriers, sharing data must be organised efficiently and responsibly, but much faster and more efficiently than we are used to at the moment. Increased access to data means an acceleration of AI implementation and higher accuracy, which results in overall better service. Trust, insight, more knowledge, and our democratic principles form the basis.


    Any organisation must always carefully weigh and categorise opportunities and risks to protect business interests and ensure that their data is not misused. The question is: how are we going to feed AI applications with data with these restrictions in place?

    The Netherlands AI Coalition wants to simplify data sharing, a crucial element of AI, by researching, further developing, and deploying privacy-enhancing techniques (PETs). Knowledge about what is and is not allowed should be readily available. Besides, ecosystems around a specific sector or region must be encouraged to consider data sharing solutions.

    Working group

    The Data Sharing working group provides participants with knowledge and resources around responsible data sharing. There are also training courses for different target groups. An extensive report is readily available containing detailed information about data sharing in AI, the possibilities, the preconditions, and which processes can be followed. The knowledge base is put into practice through use cases. These use cases are identified per sector and carried out by the member organisations.

    Inventory European Landscape, AI startups: put yourself on the map.

    Share on linkedin
    Share on twitter
    Share on whatsapp
    Share on email
    Share on print

    Gerelateerde projecten

    Pods embed error: Pod not found

    Building a digital future together