Most companies are increasingly trying to leverage data science to achieve their business goals, but it's easy to create biased models that disadvantage underrepresented groups. In this episode I get to chat with Labhesh Patel, CTO and Chief Scientist at Jumio. We start by looking at common types of data bias around representation and features that may be highly correlated with protected features (zip codes that are highly correlated with race, for example) and the challenges of supervised learning where the team tagging your data can also introduce bias into models. We then discuss the benefits of increasing the diversity and inclusiveness of both your tagging and modeling teams and questions you can ask as an engineering leader to make sure your data science team is being thoughtful about the potential impact of the models they're building.