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Bias Correction: Solutions for Socially Responsible Data Science

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.


January 25: Olga Russakovsky, Assistant Professor, Princeton University & Co-Founder, AI4All

February 1: Mukund Sundararajan, Principal Research Scientist/Director, Google

February 8: Brian Christian, Journalist/Author (co-presented with the Mansueto Institute for Urban Innovation)

February 16*: Andrea G. Parker, Associate Professor, School of Interactive Computing, Georgia Tech

February 22: Timnit Gebru, Ethical AI Researcher

March 1: Deirdre Mulligan, Professor & Director of Berkeley Center for Law & Technology, UC Berkeley

March 8: Kate Crawford, Distinguished Research Professor, NYU & Senior Principal Researcher, Microsoft Research


All talks Monday afternoon, 3:00 – 4:00 pm CST, on Zoom and YouTube.

* – This talk takes place on a Tuesday, 11:00 am – 12:00 pm CST.