Analysing & Preventing Unconscious Bias in Machine Learning. Rachel Thomas

Date: 
Friday, October 19, 2018 - 12:30
Source: 
University of San Francisco Seminar Series in Analytics
Attendees: 
215
City: 
San Francisco

**All talks are streamed live on our Facebook page at facebook.com/usfca.msds/. Coffee is provided at the in-person seminar**

Speaker: Rachel Thomas. Fast.ai and USF

Title: "Analysing & Preventing Unconscious Bias in Machine Learning"

Abstract: Increasingly AI is finding its way into nearly every product we use (everything from photo sharing apps to criminal justice decision algorithms), but often various types of bias are buried in the underlying data and models. This can have a damaging impact on both individuals and society. Through the lens of 3 case studies, we will look at how to diagnose bias, identify some sources, and some steps towards addressing it.

Bio:
Rachel Thomas was selected by Forbes as one of 20 Incredible Women in AI, earned her math PhD at Duke, and was an early engineer at Uber. She is a professor at the University of San Francisco and co-founder of fast.ai, which created the “Practical Deep Learning for Coders” course that over 100,000 students have taken. Rachel is a popular writer and keynote speaker. Her writing has been read by over half a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 7x.

101 Howard St, University of San Francisco - Downtown Campus, San Francisco, CA 94105

101 Howard St, University of San Francisco - Downtown Campus, San Francisco, CA 94105