On Bias and Fairness in NLP: Investigating the Impact of Bias and Debiasing in Language Models on the Fairness of Toxicity Detection

Understand

Language models are the new state-of-the-art natural language processing (NLP) models and they are being increasingly used in many NLP tasks.

• Even though there is evidence that language models are biased, the impact of that bias on the fairness of downstream NLP tasks is still understudied.
• Furthermore, despite that numerous debiasing methods have been proposed in the literature, the impact of bias removal methods on the fairness of NLP tasks is also understudied.
• In this work, we investigate three different sources of bias in NLP models, i.e.