Workshop – Algorithmic discrimination by Janneke Gerards and Rapaële Xenidis
The basis for this workshop was the report written for the network by Janneke Gerards and Raphaële Xenidis. In the report the authors identify what gaps and weaknesses arise from the interaction between (1) the specific types of discrimination produced by algorithmic decision-making systems and (2) the particular material and personal scope of the existing legislative framework.
In the workshop presentation it was first explained what algorithms are and what algorithmic discrimination is, how algorithms work and what challenges they pose in the context of gender equality and non-discrimination law. Examples were given where cognitive bias (often unintentional) resulted in biased design of algorithms and/or feeding the algorithms with biased data. Addressing the question of whether the current EU gender equality and non-discrimination legislative framework can adequately respond to the challenges of algorithmic discrimination, the current shortcomings in EU gender equality and non-discrimination law were described. Subsequently, an overview was given of the examples of algorithmic discrimination and related challenges identified by the network’s national experts in gender equality law that served as input for the report. An important finding is that challenges are often identified by national policy makers, academics and others, but that legislative response is quite limited, which is not surprising since national equality and non-discrimination law is a mirror image of EU law. However, legal solutions and accompanying policy measures and good practices that exist at EU and Member State level are detailed in the report. Nevertheless, the conclusion was that protection against algorithmic discrimination needs to be enhanced by a triangular approach of law, technology and knowledge.
Presentation Janneke Gerards and Raphaële Xenidis Workshop Algorithmic discrimination (WEBM)
Presentation Janneke Gerards and Raphaële Xenidis Workshop Algorithmic discrimination (PDF)
