Slovene and Croatian word embeddings in terms of gender occupational analogies

In recent years, the use of deep neural networks and dense vector embeddings for text representation have led to excellent results in the field of computational understanding of natural language. It has also been shown that word embeddings often capture gender, racial and other types of bias. The article focuses on evaluating Slovene and Croatian word embeddings in terms of gender bias using word analogy calculations. We compiled a list of masculine and feminine nouns for occupations in Slovene and evaluated the gender bias of fastText, word2vec and ELMo embeddings with different configurations and different approaches to analogy calculations. The lowest occupational gender bias was observed with the fastText embeddings. Similarly, we compared different fastText embeddings on Croatian occupational analogies

Algorithmic Discrimination in Europe:Challenges and Opportunities for Gender Equality and Non-Discrimination Law

This report investigates how algorithmic discrimination challenges the set of legal guarantees put in place in Europe to combat discrimination and ensure equal treatment. More specifically, it examines whether and how the current gender equality and non-discrimination legislative framework in place in the EU can adequately capture and redress algorithmic discrimination. It explores the gaps and weaknesses that emerge at both the EU and national levels from the interaction between, on the one hand, the specific types of discrimination that arise when algorithms are used in decision-making systems and, on the other, the particular material and personal scope of the existing legislative framework. This report also maps out the existing legal solutions, accompanying policy measures and good practice to address and redress algorithmic discrimination both at EU and national levels. Moreover, this report proposes its own integrated set of legal, knowledge-based and technological solutions to the problem of algorithmic discrimination