Data Collaboratives with the Use of Decentralised Learning.

The endeavor to find appropriate data governance frameworks capable of reconciling conflicting interests in data has dramatically gained importance across disciplines and has been discussed among legal scholars, computer scientists as well as policy-makers alike. The predominant part of the current discussion is centered around the challenging task of creating a data governance framework where data is 'as open as possible and as closed as necessary'. In this article, we elaborate on modern approaches to data governance and their limitations. It analyses how propositions evolved from property rights in data towards the creation of data access and data sharing obligations and how the corresponding debates reflect the difficulty of developing approaches that reconcile seemingly opposite objectives - such as giving individuals and businesses more control over 'their' data while at the same time ensuring its availability to different stakeholders. Furthermore, we propose a wider acknowledgement of data collaboratives powered by decentralised learning techniques as a possible remedy to the shortcomings of current data governance schemes. Hence, we propose a mild formalization of the set of existing technological solutions that could inform existing approaches to data governance issues. Our proposition is based on an abstractive notion of collaborative computation as well as on several principles that are essential for our definition of data collaboratives. By adopting an interdisciplinary perspective on data governance, this article highlights how innovative technological solutions can enhance control over data while at the same time ensuring its availability to other stakeholders and thereby contributing to the achievement of the policy goals of the European Strategy for Data.