3:00–4:00 pm
Online
Join us for an exciting lecture from Brian Christian, author of The Alignment Problem: Machine Learning and Human Values.
Abstract: With the incredible growth of machine learning over recent years has come an increasing concern about whether ML systems’ objectives truly capture their human designers’ intent: the so-called “alignment problem.” Over the last five years, these questions of both ethics and safety have moved from the margins of the field to become arguably its most central concerns. The result is something of a movement: a vibrant, multifaceted, interdisciplinary effort that is producing some of the most exciting research happening today. Brian Christian, visiting scholar at UC Berkeley and author of the acclaimed bestsellers The Most Human Human and Algorithms to Live By, will survey this landscape of recent progress and the frontier of open questions that remain.
Bio: Brian Christian is the author of The Most Human Human, which was named a Wall Street Journal bestseller, a New York Times Editors’ Choice, and a New Yorker favorite book of the year. He is the author, with Tom Griffiths, of Algorithms to Live By, a #1 Audible bestseller, Amazon best science book of the year and MIT Technology Review best book of the year.
His third book, The Alignment Problem, has just been published in the US and is forthcoming in the UK and in translation in 2021.
Christian’s writing has been translated into nineteen languages, and has appeared in The New Yorker, The Atlantic, Wired, The Wall Street Journal, The Guardian, The Paris Review, and in scientific journals such as Cognitive Science. Christian has been featured on The Daily Show with Jon Stewart, Radiolab, and The Charlie Rose Show, and has lectured at Google, Facebook, Microsoft, the Santa Fe Institute, and the London School of Economics. His work has won several awards, including fellowships at Yaddo and the MacDowell Colony, publication in Best American Science & Nature Writing, and an award from the Academy of American Poets.
Born in Wilmington, Delaware, Christian holds degrees in philosophy, computer science, and poetry from Brown University and the University of Washington. A Visiting Scholar at the University of California, Berkeley, he lives in San Francisco.
Part of the CDAC Winter 2021 Distinguished Speaker Series:
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.
More information at cdac.uchicago.edu