Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

@inproceedings{Bolukbasi2016ManIT,
  title={Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings},
  author={Tolga Bolukbasi and Kai-Wei Chang and James Y. Zou and Venkatesh Saligrama and Adam Tauman Kalai},
  booktitle={NIPS},
  year={2016}
}
The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify… CONTINUE READING

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