On the Origins of Bias in NLP through the Lens of the Jim Code
Fatma Elsafoury Gavin Abercrombie
In this paper, we trace the biases in current natural language processing
(NLP) models back to their origins in racism, sexism, and homophobia over the
last 500 years. We review literature from critical race theory, gender studies,
data ethics, and digital humanities studies, and summarize the origins of bias
in NLP models from these social science perspective. We show how the causes of
the biases in the NLP pipeline are rooted in social issues. Finally, we argue
that the only way to fix the bias and unfairness in NLP is by addressing the
social problems that caused them in the first place and by incorporating social
sciences and social scientists in efforts to mitigate bias in NLP models. We
provide actionable recommendations for the NLP research community to do so.