Attenuating Bias in Word Vectors
This addresses bias in NLP applications to reduce discrimination, but it is incremental as it builds on existing bias detection and removal techniques.
The paper tackles the problem of bias in word vectors by developing methods to detect stereotypically gendered words and remove bias from embeddings, using names as tools to identify and attenuate gender bias and extend to other biases like race, ethnicity, and age.
Word vector representations are well developed tools for various NLP and Machine Learning tasks and are known to retain significant semantic and syntactic structure of languages. But they are prone to carrying and amplifying bias which can perpetrate discrimination in various applications. In this work, we explore new simple ways to detect the most stereotypically gendered words in an embedding and remove the bias from them. We verify how names are masked carriers of gender bias and then use that as a tool to attenuate bias in embeddings. Further, we extend this property of names to show how names can be used to detect other types of bias in the embeddings such as bias based on race, ethnicity, and age.