Exploring Stereotypes and Biases in Language Technologies in Latin America

Language technologies are becoming more pervasive in our everyday lives, and they are also being applied in critical domains involving health, justice, and education. Given the importance of these applications and how they may affect our quality of life, it has become crucial to assess the errors they may make. In characterizing patterns of error, it has been found that systems obtained by machine-learning(ML) techniques from large quantities of text, such as large language models (LLMs), reproduce and amplify stereotypes. When deployed in actual applications, amplification of stereotypes can result in discriminatory behavior considered harmful in many jurisdictions.This kind of behavior is known as social bias, in that errors are distributed unevenly across social groups. Motivated by this problem, research about fairness in computational applications and in language technologies has grown considerably in recent years. However, most of this research is based on languages and representations of social biases originating in Global North cultures.