When machine learning models make decisions that affect people’s lives, how can you be sure those decisions are fair? When you build a machine learning product, how can you be sure your product isn’t biased? What does it even mean for an algorithm to be ‘fair’? As machine learning becomes more prevalent in socially impactful domains like policing, lending, and education these questions take on a new urgency. In this talk I’ll introduce several common metrics which measure the fairness of …