Accordingly, the research component in LeADS will set the theoretical framework and the practical implementation template of a common language for co-processing and joint-controlling key notions for both data scientists and jurists working at the confluence of Artificial Intelligence and Cybersecurity. Its outcomes will produce also a comparative and interdisciplinary lexicon that draws experts from these fields to define important crossover concepts. The cross-fertilization of scientific cultures is one of LeADSs flagship characteristics, generating a much needed—and currently absent—multi-level, multi-purpose common understanding of concepts useful for future researchers, policy makers, software developers, lawyers and market actors. For instance, managing and preventing personal data breaches for a company (Data Controller), is a cost to minimize in the traditional cybersecurity paradigm, but for data subjects the same data breach is an assault to their fundamental rights. The GDPR imposes to Data Controllers to minimize the risks for data subjects’ fundamental rights, eliciting an entirely new approach to risk minimization and cybersecurity.
LeADS research objectives are twofold:
LeADS aims to develop a common language that will assist producers, service providers, and consumers in managing and contracting over their information assets and data in a less antagonistic and more unitary fashion. A Common language is required for co-processing and joint-controlling key notions for both data scientists and jurists working at the confluence of Artificial Intelligence and Cybersecurity.
LeADS is animated by the idea that in the digital economy data protection holds the keys for both protecting fundamental rights and fostering the kind of competition that will sustain the growth and “completion” of the “Digital Single Market” and the competitive ability of European businesses outside the EU. Under LeADS, the General Data Protection Regulation (GDPR) and other EU rules will dictate the transnational standard for the global data economy while training researchers able to drive the process and set an example.
The articulated research programme (detailed in the ESRs’ descriptions) will move hand in hand with the training (training itself being a component of the research). The overall research network will map the conceptual gaps among the disciplines involved and produce a clear glossary to reduce misunderstandings and impracticability of adopted technical/legal solutions.
LeADS innovative theoretical model, is based on the conceptualization of deep characteristics of the data: the ease of correlating ostensibly- anonymized data to actual individuals, as addressed under different perspectives by ESRs 5, 8, 9, 11 (we call it “Un-anonymity”); the further uses of private data enabled by algorithms and machine-learning that were unexpected at time of first collection/processing (we call it “Data-Privaticity”). The interaction between un-anonymity and data privaticity can both cause direct\indirect harm, or lead to the production of private/public gains with a potential impact on various legal domains, such as competition, consumer protection, distant contracts, public and individual health, individual freedoms, etc., addressed in particular by ESRs 2, 12, 14, 15. A typical example are dark patterns: e.g. a user interface that has been carefully crafted to trick users into doing things, such as buying insurance with their purchase or signing up for recurring bills, an operation often set in motion by artificial agents using personal data analytics tools.