PAPIs Latam 2019

ML models are known to learn from the data they are submitted. However, some problems in the models can only be accounted for when they fail to express the "impartial" perspective they were supposed to have. In this sense, to get the most benefit from advances in AI and machine learning, culture, and diversity in the teams is a critical aspect to avoid data bias in many senses. Without diverse teams, the data and the learned models may drive automation to a bias that only deepens the gap.