The more digital and data-driven approach to competition law

Twenty years after the implementation of the more economic approach to competition law, time has come to revisit and adapt this approach to new digital challenges and to the evolution of our economy and society. The arrival of disruptive technologies has brought to inquiry whether the traditional approach to competition law, based on economic instruments and the consumer welfare standard, is still representative of our current society, where Artificial Intelligence (AI) is increasingly employed in decision-making processes, and information is measured in bytes. Competition law is not immune to this digital revolution, and enforcers have to deal with an emerging reality, where undertakings explore new possibilities of conducting business and where market’s structure is becoming more complex and evolving at a fast pace. Taking all these factors into consideration, this research suggests a new and innovative approach to competition law based on AI and other disruptive technologies as methodology to enforce competition law, and data as the key factor that drives the implementation of competition rules. The “more Digital and Data-Driven approach” to competition law is here suggested, as an alternative and a step forward to the traditional economic approach. Three significant aspects are the pillars of this new approach: firstly, according to the evolution of the CJEU’s jurisprudence, data protection and privacy should be considered as a paradigm when enforcing competition law (goals). Secondly, competition authorities should invest in new technologies to promote a pro-active approach to detect more efficiently competition law infringements (methodology). Thirdly, in the information age, a data-driven approach means cooperation and data sharing between competition authorities, private companies and other public bodies to exchange databases and digital tools (cooperation). This research is divided into two parts. The first part starts with an analysis of the evolution of competition law through different eras and schools of thought and how digitalisation has become an influencing factor, with algorithmic and data-driven theories of harm. Thereafter, each pillar of the proposed more Digital and Data-Driven approach is in-depth analysed. This part also considers interviews conducted with some competition authorities, whose outcomes suggest that many of them are already moving towards a more digitalised approach. The second part analyses how the more Digital and Data-Driven approach may affect fundamental rights of defence and which solutions can be implemented accordingly. On the one hand, competition authorities should take advantage of such sophisticated tools to increase their effectiveness and keep up with the digital environment in which private actors interact. On the other hand, the use of AI should not come to the detriment of rights of defence because of inscrutability of certain systems. This part is dedicated to three essential procedural rights: the right to be heard, which includes the right to access the file and relevant evidence; the right to a reasoned decision and effective judicial review; and the right to legal assistance and legal professional privilege. This thesis encourages competition authorities to adopt a new approach that should include (i) data-related concerns as additional competition law goals; (ii) a new methodology based on AI-related systems, with solutions to safely use such tools and rely on them as evidence in administrative and judicial proceedings; and (iii) enhanced cooperation in the form of data sharing between them and private companies and among public sector bodies. Overall, this thesis aims to fulfil an enforcement gap in the current antitrust system by providing a novel approach to address anticompetitive behaviours that may not be caught in the digital era. At the same time, this thesis also aims to provide enforcers with the right set of instruments to ensure that the core of fundamental rights is not set aside in the name of efficiency.