Author: Denny Lee, Brooke Wenig, Diana Pfeil
Publisher: Data Brew
Publication Year: N/A
Summary: The following video series discusses 3 large topics – transparency, privacy, and bias. First was the idea of transparency in data science – one example is that of a credit score. If you are rejected for a loan, you have a right to know why you were rejected and more specifically what factors and weights impacted that rejection. The privacy concerns brought up were surrounding websites and apps that rely on gathering our data for advertising purposes. The author recommends keeping a close eye on the European Union’s recommendations on best practices. Third, bias exists in our world. We measure our the world around us and create algorithms to predict it. Of course it returns bias. It’s important that we recognize that bias – the algorithms should not be used to codify and perpetuate bias, creating a negative feedback loop.