Current legislation is not designed for Big Data. It factors in neither the benefits nor the risks of large volumes of data. Taking the example of intelligent traffic, this research project examines four key subquestions: Who owns the data that comes from automated driving? What role does data protection play? When can data be used as evidence in criminal proceedings? And can the manufacturers or users of self-driving cars file for criminal prosecution if data is uncovered by third parties through spying and then published? Taking current law as a starting point, the project creates a framework for a group of specialists from academia and practice to draft recommendations for new statutory provisions.

Technical advances are frequently ahead of changes in the law, and this applies to the use of Big Data – e.g. from self-driving cars. These cars just happen to produce huge amounts of data that is inadequately regulated in terms of data protection or access rights. This raises numerous questions: Do users of automated driving systems need to be protected against unrestricted data evaluation? Or should they be entitled to a share in the value of the data generated during operation? And what if the reconstructed pattern of movements is of interest not only for the purposes of personalised advertising but also to the criminal prosecution authorities?

The project is divided into four individual PhD projects and an IT research package. The PhD projects tackle specific problems crucial to regulating Big Data. The first phase of the individual PhD projects, i.e. defining the approach and the structure, and reviewing the relevant literature, is completed. During the next months, the researchers will work towards completing the second phase, i.e. analysis of the literature and case studies, as well as the final phase, i.e. writing of the thesis and additional publications. First interesting results relate mainly to the definition of several legal terms, which is of high importance for the subsequent research.

The IT research package covers Big Data and traffic analysis, and prepares data sets for law research. This includes raw data from real-world examples, e.g. data from carsharing services and digitalized transportation. To collect these data sets, web data has been mined over a certain period of time before its evaporation. Under the concept of open data, the data sets are available for download on a GitHub repository (github.com/WebObservatoryUnibas).

References

• Gless, ‘Defense of the True Positives’ – Predictive Policing, in Emre Bayamlıoğlu, Irina Baraliuc, Liisa Janssens, Mireille Hildebrandt (eds). BEING PROFILED:COGITAS ERGO SUM. 10 Years of Profiling the European Citizen, Amsterdam University Press 2018 (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3297753)
• Gless/Stagno, ‘Digitale Assistenten und strafprozessuale Beweisführung’, SJZ 12/2018, 289 ff.
• Schmid/Schmidt/Zech, ‘Rechte an Daten – zum Stand der Diskussion’, sic! 11/2018, 625 ff.
• Zech, Liability for autonomous systems: Tackling specific risks of modern IT, in: Lohsse/Schulze/Staudenmayer (Hrsg.), Münster Colloquia on EU Law and the Digital Economy IV, Liability for Artificial Intelligence and the Internet of Things, 2018, (https://ssrn.com/abstract=3195676)
• Zech, Building a European Data Economy – The European Commission’s Proposal for a Data Producer’s Right, Zeitschrift für Geistiges Eigentum/Intellectual Property Journal (ZGE/IPJ) 9 (2017), 317-330
• Schmidt/Zech, Datenbankherstellerschutz für Rohdaten?, Computer und Recht (CR) 2017, 417-426

About the project

• Rechtliche Herausforderungen bei Big Data: Vorteile zuweisen, Risiken minimieren, blog post on 27 November 2018
• The legal challenges posed by Big Data: questions of exploitation and protection, project description on www.nrp75.ch