Summer 2019

Summer Cluster: Fairness

May 29Jul. 9, 2019

The Fairness cluster brought together a variety of perspectives on defining and achieving fairness goals in automated decision-making systems. Such systems are commonly used for binary classification tasks — predicting recidivism, creditworthiness, hirability, etc. of individuals. Individual fairness notions demand that similar individuals be treated similarly by the classification system. Group fairness notions seek to achieve some measure of statistical parity for protected groups vis-à-vis the general population. To overcome shortcomings in these definitions, intermediate notions of fairness such as multicalibration and multimetric fairness have been defined to protect all sufficiently large, computable groups. Much work needs to be done in fine-tuning and applying these definitions to new scenarios.

While we have a good understanding of fairness for one binary classifier, real-world systems involve multiple classifiers classifying individuals in parallel (college admissions, ads shown) or in a pipeline (college admission followed by employment). Work on developing appropriate notions of fairness in these settings is in its infancy and will be further developed by this program.

In all these settings, we seek not only to design fair(er) decision procedures but also to understand computational and informational limitations that prevent us from doing so. Such negative results tell us what assumptions about the model need to change to achieve fairness and drive us to define approximate notions of fairness that can be achieved.

We also viewed fairness through an economic lens, understanding the causes for rational agents to be unfair and the costs of incentivizing such agents to behave fairly.

Long-term visitors to the cluster were primarily theoretical computer scientists who had been working on such questions. The cluster included two workshops. The first brought together scholars from the humanities, social sciences, law, and medicine to discuss phenomena of interest to their fields from the point of view of fairness. This provided theoretical computer scientists with a rich source of important problems to think about. The second workshop was more typical — with presentations by long-term visitors and people invited just for this workshop — on technical results on fairness.

This program was supported in part by the Patrick J. McGovern Foundation.

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Cynthia Dwork (Harvard University & Microsoft Research), Sampath Kannan (University of Pennsylvania), Jamie Morgenstern (University of Pennsylvania)

Long-Term Participants (including Organizers):

Allan Borodin (University of Toronto), Elisa Celis (Yale University), Rachel Cummings (Georgia Institute of Technology), Cynthia Dwork (Harvard University & Microsoft Research), Sorelle Friedler (Haverford College), Shafi Goldwasser (UC Berkeley), Swati Gupta (Georgia Tech), Moritz Hardt (UC Berkeley), Nicole Immorlica (Microsoft Research), Sampath Kannan (University of Pennsylvania), Jon Kleinberg (Cornell University), Aleksandra Korolova (University of Southern California), Stefano Leonardi (Sapienza University of Rome), Katrina Ligett (Hebrew University of Jerusalem), Jamie Morgenstern (University of Pennsylvania), Deirdre Mulligan (UC Berkeley), Moni Naor (Weizmann Institute of Science), Helen Nissenbaum (Cornell Tech), Toniann Pitassi (University of Toronto), Gireeja Ranade (UC Berkeley), Omer Reingold (Stanford University), Aaron Roth (University of Pennsylvania), Guy Rothblum (Weizmann Institute), Nathan Srebro (Toyota Technological Institute at Chicago), Pragya Sur (Stanford University), Patricia Williams (Columbia University), Steven Wu (University of Minnesota Twin Cities), Richard Zemel (University of Toronto), James Zou (Stanford University)

Visiting Graduate Students and Postdocs:

Shera Avi-Yonah (Harvard University), Yahav Bechavod (Hebrew University), Yu Chen (University of Pennsylvania), Frances Ding (Harvard University), Sumegha Garg (Princeton University), Boriana Gjura (Harvard University), Cyrus Hettle (Georgia Institute of Technology), Lily Hu (Harvard University), Christina Ilvento (Harvard University), Christopher Jung (University of Pennsylvania), Michael Kim (Stanford University), Neil Lutz (University of Pennsylvania), Charlie Marx (Haverford College), Yonadav Shavit (Harvard University), Chloe Yang (Georgia Institute of Technology), Gal Yona (Weizmann Institute)


Jun. 5Jun. 7, 2019


Cynthia Dwork (Harvard University & Microsoft Research), Patricia Williams (Columbia University)
Jul. 8Jul. 10, 2019


Moni Naor (Weizmann Institute of Science; chair), Nicole Immorlica (Microsoft Research), Steven Wu (University of Minnesota Twin Cities)

Those interested in participating in this program should send an email to the organizers at this fairness2019 [at] (at this address).