3 citations · 3 across the 1 of their papers we have counts for
5 papers
Fair Clustering: Critique, Caveats, and Future Directions
John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern +1
Clustering is a fundamental problem in machine learning and operations research. Therefore, given the fact that fairness considerations have become of paramount importance in algor…
Doubly Constrained Fair Clustering
John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern +1
The remarkable attention which fair clustering has received in the last few years has resulted in a significant number of different notions of fairness. Despite the fact that these…
Artificial Intelligence/Operations Research Workshop 2 Report Out
John Dickerson, Bistra Dilkina, Yu Ding +9
This workshop Report Out focuses on the foundational elements of trustworthy AI and OR technology, and how to ensure all AI and OR systems implement these elements in their system…
Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective
Avi Schwarzschild, Max Cembalest, Karthik Rao +2
As neural networks increasingly make critical decisions in high-stakes settings, monitoring and explaining their behavior in an understandable and trustworthy manner is a necessity…
Neural Auctions Compromise Bidder Information
Alex Stein, Avi Schwarzschild, Michael Curry +2
Single-shot auctions are commonly used as a means to sell goods, for example when selling ad space or allocating radio frequencies, however devising mechanisms for auctions with mu…