Research Area:  Machine Learning

Abstract:

Word embeddings provide quantitative representations of word semantics and the associations between word meanings in text data, including in large repositories in media and social media archives. This article introduces social psychologists to word embedding research via a consideration of bias analysis, a topic of central concern in the discipline. We explain how word embeddings are constructed and how they can be used to measure bias along bipolar dimensions that are comparable to semantic differential scales. We review recent studies that show how familiar social biases can be detected in embeddings and how these change over time and in conjunction with real‐world discriminatory practices. The evidence suggests that embeddings yield valid and reliable estimates of bias and that they can identify subtle biases that may not be communicated explicitly. We argue that word embedding research can extend scholarship on prejudice and stereotyping, providing measures of the bias environment of human thought and action.

Keywords:  

Author(s) Name:  Kevin Durrheim, Maria Schuld, Martin Canaan Mafunda, Sindisiwe Mazibuko

Publisher name:  ResearchGate

DOI:  10.1111/bjso.12560

Volume Information:  Volume 43, (2023)

Paper Link:   https://www.researchgate.net/publication/362223670_Using_word_embeddings_to_investigate_cultural_biases