AI and Authentic Comms

October 18, 2:00pm PT, Jarvis Room

Machine Learning Fairness

Throughout history, the people with power have shaped their narrative by redefining justice. Today's decision-makers are rapidly offloading analysis to algorithms, where recognizing the effects of machine output is often done only in retrospect. In this session, we will explore the relationship between human impact and mathematical representations of fairness to help us build more equitable machine learning systems.

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Emily Pries

Lyft
Teaching Engineer

Emily Pries is a Teaching Engineer at Lyft, where she helps software developers build out their technical toolkits. She believes that all of us have a stake in the future of machine learning and works to bridge the gap between development and impact. Prior to joining Lyft, she taught high school courses on machine learning and computer science. Emily holds a degree in Mathematics and Computer Science from Columbia University.

Check out all of our other speakers.