Public Safety is vital for the functioning of societies: Without safety there is no freedom, no happiness, and no prosperity. The public good of safety matters to all of us, and therefore needs to be jointly shaped and maintained by all societal partners. Data generated by multiple agents play an increasingly important role in the prevention, preparedness and mitigation of harm or disaster. The development of an ecosystem of trust regarding AI assisted public safety promotion is central to this ELSA Lab application. In a variety of use cases benefits and safeguards are analyzed against the private-public-machine agency backdrop.

In urban studies, material public space is conventionally considered as three-dimensional. With the advent of social media however, a fourth dimension can be identified; a virtual dimension that is spatially and temporally interlaced with physical space. What is the impact of this virtual dimension on the societal function of public space? This is researched theoretically and empirically, using urban sport skateboarding at the Skate-Zone in Amsterdam as case-study. Skateboarding is exemplary both of the embodied sensory use of three-dimensional physical space and the intricate engagement with the virtual realm through social media. The project set-up is explorative, interdisciplinary and multi-method.

This project will research how we can balance the benefits of using big data for citizens, businesses and governments with the risks these entail when public and private databases are combined. The goal is to design a regulatory and institutional landscape with a system of checks and balances so that the interests of all parties are served maximally, while potential harms are minimised. The research consists of two tracks: (1) Combining databases from the ?physical infrastructure? (the smart grid), with government databases from the Dutch Police (postdoc researcher); (2) Combining databases from the ?social infrastructure? (social media), with government databases from the Dutch Police (PhD student). Central research questions: (1) Which knowledge can public parties and private parties obtain from using data analytics when linking their databases? (2) How can these parties use such knowledge to create (a) novel/improved services for citizens, and for (b) fraud detection and criminal investigation? (3) How can the interests of citizens be aligned with those of the government and those of businesses in such a way that citizens benefit maximally and suffer minimal restrictions on their rights and freedoms? (4) What regulatory and institutional landscape is required to ensure that an optimal balance between citizens? interests and freedoms and the interests of businesses and governments is guaranteed? Both tracks use the following methodologies: (1) Empirical research: qualitative studies and technical research; and (2) Theoretical/legal/conceptual research: desk research, conceptual analysis and institutional design.