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

arXiv:2305.12829v3 Announce Type: replace
Abstract: 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 …

abstract art arxiv bias cs.cl detection evidence fairness impact language language models language processing natural natural language natural language processing nlp processing state tasks toxicity toxicity detection type