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.

Model interpretability is getting more and more attention both from ML practitioners and the community at large. With the GDPR and LGPD coming into force and discussions about privacy, fairness, and ethics becoming more relevant than ever, while models are getting more and more powerful with increasing complexity and opacity, how much we, as professionals and as a community, should care about model interpretability?