4 papers
Learning Complementary Policies for Human-AI Teams
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga
This paper tackles the critical challenge of human-AI complementarity in decision-making. Departing from the traditional focus on algorithmic performance in favor of performance of…
The Value of AI Advice: Personalized and Value-Maximizing AI Advisors Are Necessary to Reliably Benefit Experts and Organizations
Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang
Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Cons…
Bias-Aware Mislabeling Detection via Decoupled Confident Learning
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
Reliable data is a cornerstone of modern organizational systems. A notable data integrity challenge stems from label bias, which refers to systematic errors in a label, a covariate…
Using Machine Bias To Measure Human Bias
Wanxue Dong, Maria De-Arteaga, Maytal Saar-Tsechansky
Biased human decisions have consequential impacts across various domains, yielding unfair treatment of individuals and resulting in suboptimal outcomes for organizations and societ…