Debiasing Word Embeddings from Sentiment Associations in Names
Christoph Hube, Maximilian Idahl, Besnik Fetahu · 2020
Word embeddings, trained through models like skip-gram, have shown to be prone to capturing the biases from the training corpus, e.g. gender bias. Such biases are unwanted as they spill in downstream tasks, thus, leading to discriminatory behavior.