Wednesday, June 5 – Friday, June 7, 2019
Melvin Calvin Laboratory
University of California Berkeley
Sponsored by the Simons Institute for the Theory of Computing and the Sloan Foundation
Artificially intelligent systems extrapolate from historical training data. While the training process is robust to “noisy” data, systematically biased data will inexorably lead to biased systems. The emerging field of algorithmic fairness seeks interventions to blunt the downstream effects of data bias. Initial work has focused on classification and prediction algorithms.
This cross-cutting workshop will examine the sources and nature of racial bias in a wide range of settings such as genome-wide association studies, social and financial credit systems, bail and probate calculations, black box medicine, and facial recognition and robotic surveillance. We will survey state-of-the-art algorithmic literature, and lay a more concrete intellectual foundation for advancing the field of algorithmic fairness.
Full schedule: https://simons.berkeley.edu/workshops/schedule/10757#