Lack of diversity in data science perpetuates AI bias - SiliconANGLE

Data privacy measures such as the General Data Protection Regulation and the California Consumer Privacy Act are expanding the definition and protection of private sensitive data. Anonymization efforts, though valiant, can only go so far. "You can only manage what you measure, right?" said Hannah Sperling (pictured), business process intelligence, academic and research alliances at SAP SE. "But if everybody is afraid to touch sensitive data, we might not get to where we want to be. I've been getting into data anonymization procedures, because if we could render more workforce data usable, especially when it comes to increasing diversity in STEM or in technology jobs, we should really be letting the data speak." Sperling spoke with Lisa Martin, host of theCUBE, SiliconANGLE Media's livestreaming studio, during the Women in Data Science (WiDS) event.