About
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 examines the sources and nature of racial bias in a range of settings such as genomics, medicine, credit systems, bail and probate calculations, and automated surveillance. We will survey state-of-the-art algorithmic literature, and lay a more comprehensive intellectual foundation for advancing algorithmic fairness.
Chairs/Organizers
Invited Participants
Shera Avi-Yonah (Harvard University), Yahav Bechavod (Hebrew University), Ruha Benjamin (Princeton University), Elisa Celis (Yale University), Yiling Chen (Harvard University), Alexandra Chouldechova (Carnegie Mellon University), Rachel Cummings (Georgia Institute of Technology), Marcy Darnovsky (Center for Genetics and Society), Joan Fujimura (University of Wisconsin, Madison), Sumegha Garg (Princeton University), Marzyeh Ghassemi (University of Toronto), Boriana Gjura (Harvard University), Shafi Goldwasser (Simons Institute), Swati Gupta (Georgia Tech), Evelynn Hammonds (Harvard), Moritz Hardt (UC Berkeley), Cyrus Hettle (Georgia Institute of Technology), Lily Hu (Harvard University), Christina Ilvento (Harvard University), Christopher Jung (University of Pennsylvania), Jonathan Kahn (Mitchell Hamline School of Law), Sampath Kannan (University of Pennsylvania), Jay Kaufman (McGill University), Michael Kim (Stanford University), Issa Kohler-Hausmann (Yale Law School), Nancy Krieger (Harvard University), Katrina Ligett (Hebrew University of Jerusalem), Zachary Lipton (Carnegie Mellon University), Neil Lutz (University of Pennsylvania), Martha Minow (Harvard University), Jamie Morgenstern (University of Pennsylvania), Sendhil Mullainathan (University of Chicago), Deirdre Mulligan (UC Berkeley), Naomi Murakawa (Princeton University), Moni Naor (Weizmann Institute of Science), Alondra Nelson (Columbia University), Helen Nissenbaum (Cornell Tech), Osagie Obasogie (UC Berkeley), Aaron Panofsky (UCLA), Gireeja Ranade (UC Berkeley), Omer Reingold (Stanford University), Dorothy Roberts (University of Pennsylvania), Guy Rothblum (Weizmann Institute), Yonadav Shavit (Harvard University), Nati Srebro Bartom (Toyota Technological Institute at Chicago), Pragya Sur (Stanford University), Harriet Washington (The New York Academy of Medicine), Steven Wu (University of Minnesota Twin Cities), Chloe Yang (Georgia Institute of Technology), Gal Yona (Weizmann Institute)