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Data sharing

NL AIC Reference Guide: ‘Towards a federation of AI data spaces’

This document provides reference and guideline for developing AI data spaces, giving a rich set of methodologies, tools, processes and building blocks to support the challenges and requirements of AI.

Blue background with data

GAP analysis ‘From data sharing proofs-of-concept towards operationalization of the system architecture’

To gain insight into the opportunities and challenges for operationalisation and large-scale application of AI, the Data Sharing working group carried out a GAP analysis based on three use cases.

Responsible data sharing in AI

Together with algorithms, data is the building block for AI applications, which must function properly and responsibly. The Data Sharing working group has released this report (in Dutch) to help realise AI-implementations.