On the Origins of Bias in NLP through the Lens of the Jim Code
It highlights a foundational problem in NLP fairness for affected communities, but is incremental in its approach by synthesizing existing perspectives.
The paper traces biases in NLP models to historical social issues like racism and sexism, arguing that addressing these root causes through social science integration is necessary to fix bias.
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.