The Data Standard

Data Flattening, Data Bias, and the Genetic Non-Discrimination Act

Fernando Schwartz, Head of AI at Citius Tech shares his thoughts on Data Flattening, Data Bias and The Genetic Non-Discrimination Act.

Episode Summary

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.

Meet The Host

Darren Kaplan

Co-Founder & Board Member of HiQ Labs

Darren Kaplan is 2x Founder and recognized as one of the Top 20 Data Science Influencers in 2020. Darren is the co-creator of The Data Standard, the premier networking user-community for data-science, data engineers, and cybersecurity enthusiasts.

Meet The Guest

Fernando Schwartz

Fernando Schwartz is a data scientist with years of experience in developing and deploying cutting-edge AI and machine learning solutions in healthcare. He recently became the Global Head of Data Science at Merck and held an honorary Adjunct Professorship at the University of Tennessee.