This article discusses transparency obligations introduced in the Artificial Intelligence Act, the recently proposed European regulatory framework for artificial intelligence (AI). An analysis of the extent to which current approaches for AI documentation satisfy requirements is presented and their suitability as a basis for future technical standards is assessed.

Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover products. In concrete terms, we assess the practical and computational aspects of multi-class Sentinel-2 image segmentation based on a convolutional neural network and random forest approaches. The annotated learning set derives from data that is made available as result of the implementation of European Union’s INSPIRE Directive. Since this network of data sets remains incomplete in regard to some geographic areas, another objective of this work was to provide consistent and reproducible ways for machine-driven mapping of these gaps and a potential update of the existing ones. Finally, the performance analysis identifies the most important hyper-parameters, and provides hints on the models’ deployment and their transferability.

Nowadays, Artificial Intelligence (AI) is present in all sectors of the economy. Consequently, both data-the raw material used to build AI systems- and AI have an unprecedented impact on society and there is a need to ensure that they work for its benefit. For this reason, the European Union has put data and trustworthy AI at the center of recent legislative initiatives. An important element in these regulations is transparency, understood as the provision of information to relevant stakeholders to support their understanding of AI systems and data throughout their lifecycle. In recent years, an increasing number of approaches for documenting AI and datasets have emerged, both within academia and the private sector. In this work, we identify the 36 most relevant ones from more than 2200 papers related to trustworthy AI. We assess their relevance from the angle of European regulatory objectives, their coverage of AI technologies and economic sectors, and their suitability to address the specific needs of multiple stakeholders. Finally, we discuss the main documentation gaps found, including the need to better address data innovation practices (e.g. data sharing, data reuse) and large-scale algorithmic systems (e.g. those used in online platforms), and to widen the focus from algorithms and data to AI systems as a whole.