no code implementations • 16 Feb 2024 • Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
There, unlike in classical crowdsourced ML, participants deliberately specialize their efforts by working on subproblems, such as demographic subgroups in the service of fairness.
1 code implementation • 31 Jan 2023 • Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
Using this characterization, we give an exceedingly simple algorithm that can be analyzed both as a boosting algorithm for regression and as a multicalibration algorithm for a class H that makes use only of a standard squared error regression oracle for H. We give a weak learning assumption on H that ensures convergence to Bayes optimality without the need to make any realizability assumptions -- giving us an agnostic boosting algorithm for regression.
no code implementations • 15 Sep 2022 • Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
We show how to take a regression function $\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints.
no code implementations • 25 Jan 2022 • Ira Globus-Harris, Michael Kearns, Aaron Roth
We propose and analyze an algorithmic framework for "bias bounties": events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security.
1 code implementation • 18 Jun 2021 • Joerg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam Smith
Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data.
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).
no code implementations • 1 Mar 2019 • Marika Swanberg, Ira Globus-Harris, Iris Griffith, Anna Ritz, Adam Groce, Andrew Bray
Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines.