3 citations · 5 across the 5 of their papers we have counts for
5 papers
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…
Targets in Reinforcement Learning to solve Stackelberg Security Games
Saptarashmi Bandyopadhyay, Chenqi Zhu, Philip Daniel +3
Reinforcement Learning (RL) algorithms have been successfully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, et…
Repairing Regressors for Fair Binary Classification at Any Decision Threshold
Kweku Kwegyir-Aggrey, A. Feder Cooper, Jessica Dai +3
We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds. By decreasing the statistical dist…
Counterfactual Explanations for Machine Learning: Challenges Revisited
Sahil Verma, John Dickerson, Keegan Hines
Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models. They provide ``what if'' feedback of the form `…