5 citations · 10 across the 16 of their papers we have counts for
6 papers · 1 filter
Doubly-Regressing Approach for Subgroup Fairness
Kunwoong Kim, Kyungseon Lee, Jihu Lee +2
Algorithmic fairness is a socially crucial topic in real-world applications of AI. Among many notions of fairness, subgroup fairness is widely studied when multiple sensitive attri…
Fair Bayesian Model-Based Clustering
Jihu Lee, Kunwoong Kim, Yongdai Kim
Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that…
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics
Insung Kong, Kunwoong Kim, Yongdai Kim
AI fairness, also known as algorithmic fairness, aims to ensure that algorithms operate without bias or discrimination towards any individual or group. Among various AI algorithms,…
Fair Clustering via Alignment
Kunwoong Kim, Jihu Lee, Sangchul Park +1
Algorithmic fairness in clustering aims to balance the proportions of instances assigned to each cluster with respect to a given sensitive attribute. While recently developed fair…
ReLU integral probability metric and its applications
Yuha Park, Kunwoong Kim, Insung Kong +1
We propose a parametric integral probability metric (IPM) to measure the discrepancy between two probability measures. The proposed IPM leverages a specific parametric family of di…
Fairness Through Matching
Kunwoong Kim, Insung Kong, Jongjin Lee +3
Group fairness requires that different protected groups, characterized by a given sensitive attribute, receive equal outcomes overall. Typically, the level of group fairness is mea…