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 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.
All events take place in the Calvin Lab auditorium.
Further details about this workshop will be posted in due course. Enquiries may be sent to simonsevents [at] berkeley [dot] edu (subject: FAIR19-1%20Web%20Inquiry) .
Registration is required to attend this workshop. Space may be limited, and you are advised to register early. To submit your name for consideration, please register and await confirmation of your acceptance before booking your travel.
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), Jessie Daniels (CUNY Hunter College), Marcy Darnovsky (Center for Genetics and Society), Sorelle Friedler (Haverford College), 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), Jon Kleinberg (Cornell University), Issa Kohler-Hausmann (Yale Law School), Aleksandra Korolova (University of Southern California), Nancy Krieger (Harvard University), Stefano Leonardi (Sapienza University of Rome), Katrina Ligett (Hebrew University of Jerusalem), Zachary Lipton (Carnegie Mellon University), Neil Lutz (University of Pennsylvania), Charlie Marx (Haverford College), Martha Minow (Harvard University), Moni Naor (Weizmann Institute of Science), Alondra Nelson (Columbia University), Helen Nissenbaum (Cornell Tech), Osagie Obasogie (UC Berkeley), Aaron Panofsky (UCLA), Toni Pitassi (University of Toronto), Gireeja Ranade (UC Berkeley), Jennifer Reardon (UCSC), Omer Reingold (Stanford University), Dorothy Roberts (University of Pennsylvania), Aaron Roth (University of Pennsylvania), Guy Rothblum (Weizmann Institute), Yonadav Shavit (Harvard University), Tania Simoncelli (Broad Institute), Nati Srebro Bartom (Toyota Technological Institute at Chicago), Pragya Sur (Stanford University), Harriet Washington (-), Steven Wu (University of Minnesota Twin Cities), Chloe Yang (Georgia Institute of Technology), Gal Yona (Weizmann Institute), Richard Zemel (University of Toronto), James Zou (Stanford University)