Description
Within the last few years, researchers have come to understand that machine learning systems may display discriminatory behavior with regards to certain protected characteristics, such as gender or race. To combat these harmful behaviors, we have created multiple definitions of fairness to enable equity in machine learning algorithms. In this talk, I will cover these different definitions of algorithmic fairness and discuss both the strengths and limitations of these formalizations. In addition, I will cover other best practices to better mitigate the unintended bias of data products.