A system allocates resources (jobs or credit) unfairly to different groups. For example algorithms that use embeddings as part of a search for hiring potential programmers or doctors might thus incorrectly downweight documents with women’s names.

Allocational harm

Embeddings don’t just reflect the statistics of their input, but also amplify bias; gendered terms become more gendered in embedding space than they bias amplification were in the input text statistics, and biases are more exaggerated than in actual labor employment statistics.

Bias amplification

A harm caused by representational harm a system demeaning or even ignoring some social groups. For example African-American names like ‘Leroy’ and ‘Shaniqua’ had a higher GloVe cosine with unpleasant words while European-American names (‘Brad’, ‘Greg’, ‘Courtney’) had a higher cosine with pleasant words.

Representational harm

Researches are try to debias by developing a transformation of the embedding space that removes gender stereotypes but preserves definitional gender or changing the training procedure. However, although debiasing these sorts of debiasing may reduce bias in embeddings, they do not eliminate it, and this remains an open problem.

Debiasing in word embeddings

In addition to their ability to learn word meaning from text, embeddings, alas, also reproduce the implicit biases and stereotypes that were latent in the text. As the prior section just showed, embeddings can roughly model relational similarity: ‘queen’ as the closest word to ‘king’ -‘man’ + ‘woman’ implies the analogy man:woman::king:queen.

University of Michigan - Ann Arbor

In a simple word,  word embeddings are numerical representations of texts.
In detail, when computer treat differents words, they will turn them into some atomic symbols. e.g. cat might be a, dogs might be b. But how can computer find the relationship between them? That's what word embeddings are. They willl build some relationships between words.

Word Embeddings

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf

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