All models of legal governance and most regulatory options have to do with ‘top-down’ solutions as an essential ingredient of the approach. Such models may include ‘bottom-up’ forms of self-regulation, such as in forms of ex post regulation, or unenforced self-regulation. This paper focuses on what lies in between such top-down and bottom-up approaches, namely, the middle-out interface of the analysis. Within the EU legal framework, this middle-out layer is mainly associated with forms of co-regulation, as defined by Recital 44 of the 2010 AVMS Directive and Article 5(2) of the GDPR. However, there are also additional models on how we should grasp the middle-out layer of legal regulation, as shown by the debates on the governance of AI and the Web of Data. For example, the debates on issues such as monitored self-regulation, coordination mechanisms for good AI governance, and ‘wind-rose’ models for the Web of Data make it clear that co-regulation is not the only alternative to both bottom-up and top-down approaches. From a methodological viewpoint, the middle-out approach sheds light on three different kinds of issues that regard (i) how to strike a balance between multiple regulatory systems; (ii) how to align primary and secondary rules of the law; and (iii) how to properly coordinate bottom-up and top-down policy choices. The increasing complexity of technological regulation recommends new models of governance that revolve around this middle-out analytical ground.

Ontology-driven systems with reasoning capabilities in the legal field are now better understood. Legal concepts are not discrete, but make up a dynamic continuum between common sense terms, specific technical use, and professional knowledge, in an e

Recent advances on information technologies and communications, coupled with the advent of the social media applications have fuelled a new landscape of emergency and disaster response systems by enabling affected citizens to generate georeferenced real time information on critical events. The identification and analysis of such events is not straightforward and the application of crowdsourcing methods or automatic tools is needed for that purpose. Whereas crowdsourcing makes emphasis on the resources of people to produce, aggregate, or filter original data, automatic tools make use of information retrieval techniques to analyze publicly available information. This paper reviews a set of online tools and platforms implemented in recent years which are currently being applied in the area of emergency management and proposes a taxonomy for its categorization.

This open access book shows the factors linking information flow, social intelligence, rights management and modelling with epistemic democracy, offering licensed linked data along with information about the rights involved. This model of democracy for the web of data brings new challenges for the social organisation of knowledge, collective innovation, and the coordination of actions. Licensed linked data, licensed linguistic linked data, right expression languages, semantic web regulatory models, electronic institutions, artificial socio-cognitive systems are examples of regulatory and institutional design (regulations by design). The web has been massively populated with both data and services, and semantically structured data, the linked data cloud, facilitates and fosters human-machine interaction. Linked data aims to create ecosystems to make it possible to browse, discover, exploit and reuse data sets for applications. Rights Expression Languages semi-automatically regulate the use and reuse of content.