"Learning to Advise Humans in High-Stakes Settings". With Nicholas Wolzcynski and Tong Wang. https://arxiv.org/abs/2210.12849
Maria De-Arteaga, Stefan Feuerriegel, and Maytal Saar-Tsechansky. Production and Operation Management, 2022.
"Human-AI Collaboration with Bandit Feedback”, with Ruijiang Gao, Maria De-Arteaga, Matt Lease, and Min Kyung Lee. International Joint Conference on Artificial Intelligence (IJCAI-2021), 2021
Augmented Fairness, with Tong Wang
In this stream of work my co-author. Tong Wang and I focus on productive social values, how they can be effectively integrated into machine learning and AI systems and method, and how can ML augment humans' fairness in decision-making. Given humans exhibit bias, but are often unaware of their biases, we consider new ML frameworks that augment humans and making decision more fair.
In this paper Tong Wang and I focus on developing ML to augment human fairness. By contrast to most prior work which focuses on the (important!) problem of algorithmic fairness, we consider settings where humans decision makers exhibit bias, and we propose a machine learning framework to augment humans so that the final decisions have a superior fairness-accuracy tradeoff.
Publications:
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness", with Tong Wang, Best Paper Award, INFORMS Workshop on Data Science, 2020.
NeurIPS2020, Algorithmic Fairness through the Lens of Causality and Interpretability