Recorded 11 July 2022. Cynthia Dwork of Harvard University SEAS presents "Group Fairness and Individual Fairness" at IPAM's Graduate Summer School on Algorithmic Fairness. Abstract: The early literature on the theory of algorithmic fairness identified two categories of fairness notions: group fairness, which requires that certain statistics be similar on different demographic groups, and individual fairness, which requires that individuals who are “similar,” with respect to a given scoring or classification task, be treated similarly. We will discuss these notions and their pros and cons. Learn more online at: http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-on-algorithmic-fairness/