Year: 2019
Type: article
Abstract: This paper investigates the presence of gender bias in pretrained Swedish embeddings.We focus on a scenario where names are matched with occupations, and we demonstrate how a number of standard pretrained embeddings handle this task.Our experiments show some significant differences between the pretrained embeddings, with word-based methods showing the most bias and contextualized language models showing the least.We also demonstrate that a previously proposed debiasing method does not affect the performance of the various embeddings in this scenario.
Authors: Magnus Sahlgren, Fredrik Olsson
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2982388861