"Liability for Breach of Fundamental Rights: The Case of Algorithmic Discrimination"

• Jurisdiction: European Union
• Year: 2023

Speaker

Raphaële Xenidis is currently a Ph.D. candidate in the Law Department at the European University Institute, specializing in intersectional discrimination and equality law in Europe under the guidance of Prof. Claire Kilpatrick. With a background in political science, Raphaële earned their Master's Degree from Sciences Po Lille in France and the Westfälische Wilhelms-Universität in Germany.

Following specialization in international human rights law at SAIS Europe, Johns Hopkins University, where Raphaële obtained a Master's degree in international affairs, they pursued an LL.M. in Comparative, European, and International laws from the EUI. The Ph.D. journey commenced in 2014, marked by diverse experiences, including a research fellowship at Columbia Law School in New York from 2016 to 2017, supported by the Fulbright-Schuman scholarship.

Since June 2018, Raphaële has held the role of a postdoctoral researcher in gender equality law at Utrecht University in the Netherlands. Simultaneously, as a member of the coordination team of the gender equality stream within the European network of legal experts, they contribute valuable insights and advice to the European Commission on national developments in gender equality and non-discrimination law in Europe.

Topic

In the e-presentation delivered by Raphaële Xenidis, a comprehensive exploration of various pertinent issues unfolds. The focal points of discussion encompass a nuanced examination of machine discrimination, the conceptual understanding of discrimination, the available legal remedies, and the intricate interplay between European Union (EU) anti-discrimination law and the EU Artificial Intelligence Act (AIA) and the Artificial Intelligence Liability Directive (AILD).

To begin with, Xenidis delves into the intricate question of how machines engage in discrimination. This involves an exploration of the mechanisms and algorithms that underlie machine decision-making processes, shedding light on the inherent biases and potential pitfalls embedded in these systems. By unraveling the intricacies of machine discrimination, the presentation aims to foster a deeper understanding of the challenges...