On the Origins of Bias in NLP through the Lens of the Jim Code

TL;DR

This paper explores the social origins of bias in NLP models, linking them to historical social issues like racism and sexism, and advocates for integrating social sciences to effectively address these biases.

Contribution

It provides a social science perspective on NLP bias origins and offers actionable recommendations for incorporating social science insights into bias mitigation.

Findings

Bias in NLP stems from historical social issues.

Addressing social problems is essential for bias mitigation.

Integrating social sciences can improve fairness in NLP.

Abstract

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.