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As natural language processing systems become more widespread, it isnecessary to address fairness issues in their implementation and deployment toensure that their negative impacts on society are understood and minimized.However, there is limited work that studies fairness using a multilingual andintersectional framework or on downstream tasks. In this paper, we introducefour multilingual Equity Evaluation Corpora, supplementary test sets designedto measure social biases, and a novel statistical framework for studyingunisectional and intersectional social biases in natural language processing.We use these tools to measure gender, racial, ethnic, and intersectional socialbiases across five models trained on emotion regression tasks in English,Spanish, and Arabic. We find that many systems demonstrate statisticallysignificant unisectional and intersectional social biases.

Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic