A Topography of Data Commons: From Regulation to Private Dynamism

Knowledge is subject to enclosure through digital technology and legal rules. Data collected, stored and pooled by the Internet of Things (IoT) or Artificial Intelligence (AI) are no exception to this. Operators acting in the markets related to the algorithmic society may have a quite diversified range of intellectual property rights (IPRs) to protect the information they produce and manage. This is exploited through algorithmic processing techniques, aggregating collected data for the generation of new ones, thus creating additional information and knowledge. This paper studies whether and when data, information and knowledge, presented within the Big Data, IoT and AI structures, may be considered and exploited as commons. The analysis is not aimed at stating that commons should be the general solution for the algorithmic society. Nor does it endorse legal interpretations unilaterally favoring openness and limiting IPR protection and privacy rules (though this could be the case under certain circumstances). The question is to establish whether a certain level of commons should be provided by regulation or left to spontaneous private initiatives. In this regard, two different meanings of data commons are used in this work. The first one refers to the open access systems provided by regulation, equivalent to a public domain protection, and opposed to exclusivity mechanisms. The second refers to data commons which are privately ‘constructed’ on top of background regulation and manage resources for a limited set of claimants.