  Predictive algorithms have a powerful potential to offer benefits in areas as
varied as medicine or education. However, these algorithms and the data they
use are built by humans, consequently, they can inherit the bias and prejudices
present in humans. The outcomes can systematically repeat errors that create
unfair results, which can even lead to situations of discrimination (e.g.
gender, social or racial). In order to illustrate how important is to count
with a diverse training dataset to avoid bias, we manipulate a well-known
facial expression recognition dataset to explore gender bias and discuss its
implications.
