Societal Versus Encoded Stereotypes in Text Encoders

Yacine Gaci, Boualem Benatallah, Fabio Casati, Khalid Benabdeslem

Abstract: Text encoders have recently been convicted of en-coding unjustified social stereotypes, which lead models to make biased and prejudiced predictions when trained on downstream tasks such as sentiment analysis or question answering. The presence of bias in NLP models is dangerous since it promotes the divide between different social groups. Thus, attempts at mitigating biases from NLP models constitute an active line of research. However, these methods assume that models replicate exactly the same stereotypes ingrained in society’s impression, leading to potential inaccuracies in the normative framing of bias. In this work we confirm that text encoders are indeed biased. Nonetheless, we show that encoded biases are slightly different from survey-based biases proper to human prejudice. We ground our findings on the Stereotype Content Model, an acclaimed framework to interpret stereotypes, prejudice and inter-group relations in social psychology.