no code implementations • 26 Jun 2023 • Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth
Importantly, we require that the proxy classification itself not reveal significant information about the sensitive group membership of any individual sample (i. e., it should be sufficiently non-disclosive).
no code implementations • 19 Jun 2023 • Alexander Williams Tolbert, Emily Diana
We consider the problem of learning from data corrupted by underrepresentation bias, where positive examples are filtered from the data at different, unknown rates for a fixed number of sensitive groups.
no code implementations • 9 Jul 2021 • Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi
The goal of the proxy is to allow a general "downstream" learner -- with minimal assumptions on their prediction task -- to be able to use the proxy to train a model that is fair with respect to the true sensitive features.
no code implementations • 16 Feb 2021 • Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short).
1 code implementation • 5 Nov 2020 • Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth
We consider a recently introduced framework in which fairness is measured by worst-case outcomes across groups, rather than by the more standard differences between group outcomes.
1 code implementation • 12 Jun 2020 • Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
We consider a variation on the classical finance problem of optimal portfolio design.
no code implementations • 12 Dec 2019 • Emily Diana, Michael Kearns, Seth Neel, Aaron Roth
We consider a fundamental dynamic allocation problem motivated by the problem of $\textit{securities lending}$ in financial markets, the mechanism underlying the short selling of stocks.