Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types.

ABSTRACT: Concerns about gender bias in word embedding models have captured substantial attention in the algorithmic bias research literature. Other bias types however have received lesser amounts of scrutiny. This work describes a large-scale analysis of sentiment associations in popular word embedding models along the lines of gender and ethnicity but also along the less frequently studied dimensions of socioeconomic status, age, physical appearance, sexual orientation, religious sentiment and political leanings. Consistent with previous scholarly literature, this work has found systemic bias against given names popular among African-Americans in most embedding models examined. Gender bias in embedding models however appears to be multifaceted and often reversed in polarity to what has been regula

SUBMITTER: Rozado D 

PROVIDER: S-EPMC7173861 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature