Assessing Social and Intersectional Biases in Contextualized Word Representations

Understand

Social bias in machine learning has drawn significant attention, with work ranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for different contexts, to developing algorithms to mitigate bias.

• In natural language processing, gender bias has been shown to exist in context-free word embeddings.
• Recently, contextual word representations have outperformed word embeddings in several downstream NLP tasks.
• These word representations are conditioned on their context within a sentence, and can also be used to encode the entire sentence.