The landscape of data and AI documentation approaches in the European policy context

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.

Automated summary

Nowadays, AI is widely used in various sectors, and the European Union has placed data and trustworthy AI at the center of recent legislative initiatives. This work evaluates the 36 most relevant approaches for documenting AI and datasets, considering European regulatory objectives, coverage of AI technologies and economic sectors, and suitability for addressing stakeholders' needs. The main documentation gaps, including data innovation practices and large-scale algorithmic systems, are also discussed.