Artificial Intelligence research and industrial developments have made great strides in recent years becoming increasingly pervasive within society, given the diffusion of AI applications with the aim of automating processes and decisions. One of the key elements of AI-based technologies is data, which play a central role in the quality of software outcomes. It is therefore becoming increasingly important to ensure that AI practitioners are fully aware of the quality of datasets and of the process generating them, in such a way that all the ¿typically implicit¿ assumptions, ethical issues, modeling choices clearly and transparently emerge, and their impact to downstream effects can be tracked, analysed and possibly mitigated.