  Sentiment analysis (SA) systems are widely deployed in many of the world's
languages, and there is well-documented evidence of demographic bias in these
systems. In languages beyond English, scarcer training data is often
supplemented with transfer learning using pre-trained models, including
multilingual models trained on other languages. In some cases, even supervision
data comes from other languages. Does cross-lingual transfer also import new
biases? To answer this question, we use counterfactual evaluation to test
whether gender or racial biases are imported when using cross-lingual transfer,
compared to a monolingual transfer setting. Across five languages, we find that
systems using cross-lingual transfer usually become more biased than their
monolingual counterparts. We also find racial biases to be much more prevalent
than gender biases. To spur further research on this topic, we release the
sentiment models we used for this study, and the intermediate checkpoints
throughout training, yielding 1,525 distinct models; we also release our
evaluation code.
