Did You Just Assume My Vector? Detecting Gender Stereotypes in Word Embeddings

Abstract

Recent studies found out that supervised machine learning models can capture prejudices and stereotypes from training data. Our study focuses on the detection of gender stereotypes in relation to word embeddings. We review prior work on the topic and propose a comparative study of existing methods of gender stereotype detection. We evaluate various word embeddings models with these methods and conclude that the amount of bias does not depend on the corpora size and training algorithm, and does not correlate with embeddings performance on the standard evaluation benchmarks.