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Found 4 papers, 2 papers with code
Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings
Euclidean word embedding models such as GloVe and Word2Vec have been shown to reflect human-like gender biases.
One to rule them all: Towards Joint Indic Language Hate Speech Detection
This paper is a contribution to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) 2021 shared task.
Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings
Non-contextual word embedding models have been shown to inherit human-like stereotypical biases of gender, race and religion from the training corpora.
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings
We also propose a new bias evaluation metric - Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections.
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