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

Title
Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types
Authors
Keywords
Lexicons, Word embedding, Culture, Semantics, Professions, Machine learning algorithms, Vector spaces, African American people
Journal
PLoS One
Volume 15, Issue 4, Pages e0231189
Publisher
Public Library of Science (PLoS)
Online
2020-04-22
DOI
10.1371/journal.pone.0231189

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