Manish Nagireddy, Inkit Padhi, et al.
AIES 2025
As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information–theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and is shown to be capable of being used to derive regularizers that enforce independence and separation-based fairness criteria, which admit optimization algorithms for both discrete and continuous variables that are more computationally efficient than existing algorithms. We show that these algorithms provide smooth performance– fairness tradeoff curves and perform competitively with state-of-the-art methods on both discrete datasets (COMPAS, Adult) and continuous datasets (Communities and Crimes).
Manish Nagireddy, Inkit Padhi, et al.
AIES 2025
Yue Meng, Rameswar Panda, et al.
ICLR 2021
Inkit Padhi, Karthikeyan Natesan Ramamurthy, et al.
NeurIPS 2024
Huan Song, Jayaraman J. Thiagarajan, et al.
ICASSP 2017