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.

如今，人工智能（AI）已渗透到经济的各个领域。因此，数据（用于构建人工智能系统的原材料）和人工智能都对社会产生了前所未有的影响，需要确保它们为社会带来好处。因此，欧盟将数据和值得信赖的人工智能置于最近立法举措的中心。这些法规的一个重要要素是透明度，被理解为向相关利益相关者提供信息，以支持他们对人工智能系统和数据整个生命周期的理解。近年来，学术界和私营部门出现了越来越多记录人工智能和数据集的方法。在这项工作中，我们从 2200 多篇与可信 AI 相关的论文中找出了 36 篇最相关的论文。我们从欧洲监管目标的角度、人工智能技术和经济部门的覆盖范围以及满足多个利益相关者特定需求的适合性来评估它们的相关性。最后，我们讨论了发现的主要文档差距，包括需要更好地解决数据创新实践（例如数据共享、数据重用）和大规模算法系统（例如在线平台中使用的算法系统），以及扩大对算法和数据的关注。数据到整个人工智能系统。