The Data Standard
In this episode of The Data Standard, host Darren Kaplan talks about data flattening, data bias, and the Genetic Information Non-Discrimination Act with Fernando Schwartz, Head of AI at CitiusTech.
What excites Fernando is applying advanced AI and machine learning technologies for a good purpose. He likes mixing his work with a bit of altruism, so he’s currently working in the healthcare space.
He talks about data flattening, which is a process of packing denormalized data. It turns a stack of transactions into data that is more digestible to a machine learning algorithm so that it can make accurate predictions based on historical data.
A common problem that may arise is data bias. When training an ML model, it’s crucial to be very sensitive about the inputs so that the algorithm doesn’t have a bias. That way, it won’t pick up irrelevant stuff that could potentially have negative consequences.
Fernando also touches upon the Genetic Information Non-Discrimination Act (GINA). He says there are certain ethical and legal issues around DNA tests, but it’s not all black-and-white.
If you’re interested in hearing about it and learning more about data flattening and data bias, tune in with Darren Kaplan and Fernando Schwartz in this very interesting episode of The Data Standard.