3 papers
cs.LG2025
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…
cs.LG2023
Mitigating Label Bias via Decoupled Confident Learning
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels i…
cs.LG2022
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
An increased awareness concerning risks of algorithmic bias has driven a surge of efforts around bias mitigation strategies. A vast majority of the proposed approaches fall under o…