Seminar: Fairness, Interpretability, & Diverse Datasets Are Not Enough

July 15, 2021

The concept of “biased data” is often too generic to be useful.  Through a series of case studies, we will explore what algorithmic bias is, different types (with different causes), and debunk some common misconceptions.  We will cover why algorithmic bias is a problem worth addressing and some steps towards solutions.

Speaker bio

Dr Rachel Thomas is co-founder of, where she helped create the most popular free online course on deep learning, bringing more people around the world with diverse and non-traditional backgrounds into AI. She previously was founding director of the Center for Applied Data Ethics at the University of San Francisco, with a focus on issues of surveillance, disinformation, bias, and justice in the tech industry. Rachel earned her PhD in mathematics at Duke University, and was an early data scientist and software engineer at Uber. She was selected by Forbes as one of 20 Incredible Women in AI and was profiled in the book Women Tech Founders on the Rise.


3pm – 4pm Seminar

4pm – 5pm Networking 

This seminar will also be available as a Webinar. Webinar link: