How public datasets constrain the development of diversity-aware news recommender systems, and what law could do about it

News recommender systems increasingly determine what news individuals see online. Over the past decade, researchers have extensively critiqued recommender systems that prioritise news based on user engagement. To offer an alternative, researchers have analysed how recommender systems could support the media's ability to fulfil its role in democratic society by recommending news based on editorial values, particularly diversity. However, there continues to be a large gap between normative theory on how news recommender systems should incorporate diversity, and technical literature that designs such systems. We argue that to realise diversity-aware recommender systems in practice, it is crucial to pay attention to the datasets that are needed to train modern news recommenders. We aim to make two main contributions. First, we identify the information a dataset must include to enable the development of the diversity-aware news recommender systems proposed in normative literature. Based on this analysis, we assess the limitations of currently available public datasets, and show what potential they do have to expand research into diversity-aware recommender systems. Second, we analyse why and how European law and policy can be used to provide researchers with structural access to the data they need to develop diversity-aware news recommender systems.