Roots, Repercussions and Rectification of Bias in NLP Transfer Learning

Roots, Repercussions and Rectification of Bias in NLP Transfer Learning by David Hopes & Benjamin Ajayi-Obe

The popularization of large pre-trained language models has resulted in their increased adoption in commercial settings. However, these models are usually pre-trained on raw, uncurated corpora that are known to contain a plethora of biases. This often results in very undesirable behaviours from the model in real-world situations that can cause societal or individual harm. In this talk, we explore the sources of this bias, as well as recent methods of measuring and mitigating it.

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