From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
Our work develops new methods to (1) measure po- litical biases in LMs trained on such corpora, along social and economic axes, and (2) mea- sure the fairness of downstream NLP models trained on top of politically biased LMs. We focus on hate speech and misinformation de- tection, aiming to empirically quantify the ef- fects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks.