3:00–4:00 pm
Online
Join us for an exciting lecture from Timnit Gebru, Ethical AI Researcher
Security, privacy and bias in the context of machine learning are often treated as binary issues, where an algorithm is either biased or fair, ethical or unjust. In reality, there is a tradeoff between using technology and opening up new privacy and security risks. Researchers are developing innovative tools that navigate these tradeoffs by applying advances in machine learning to societal issues without exacerbating bias or endangering privacy and security. The CDAC Winter 2021 Distinguished Speaker Series will host interdisciplinary researchers and thinkers exploring methods and applications that protect user privacy, prevent malicious use, and avoid deepening societal inequities — while diving into the human values and decisions that underpin these approaches.
Bio: I have been a research scientist at Google in the ethical AI team. Prior to that I did a postdoc at Microsoft Research, New York City in the FATE (Fairness Transparency Accountability and Ethics in AI) group, where I studied algorithmic bias and the ethical implications underlying projects aiming to gain insights from data (see this New York Times article for an example of my work). I received my PhD from the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. My thesis pertains to using large scale publicly available images to gain sociological insight, and working on computer vision problems that arise as a result. The Economist, The New York Times and others have covered part of this work. Prior to joining Fei-Fei's lab I worked at Apple designing circuits and signal processing algorithms for various Apple products including the first iPad. I also spent an obligatory year as an entrepreneur (as all Stanford undergrads seem to do). My research was supported by the NSF foundation GRFP fellowship and the Stanford DARE fellowship.
More information at cdac.uchicago.edu