Data Protection and Machine-Learning-Supported Decision-Making at the EU Border: A Study of ETIAS and EIS Profiling

This thesis critically examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) in the European Union's information systems handling information relevant to safeguarding the Area of Freedom, Security and Justice of the European Union, focusing on the European Travel Information and Authorization System (ETIAS) and Europol Information System (EIS). It explores the intersection of technological advancements with legal frameworks, including the GDPR, EUDPR, LED and the Artificial Intelligence Act (AI Act). The study addresses challenges like algorithmic bias, data protection concerns, and interoperability issues, analyzing their implications on fundamental rights, privacy, and transparency. By analyzing ETIAS, EIS and other large-scale IT systems that are used by Europol such as SIS and VIS, this research highlights the operational benefits and ethical dilemmas of profiling systems. The findings advocate for balanced regulatory mechanisms that ensure efficiency while upholding human rights, ultimately proposing solutions to align technological innovation with ethical governance.