27 citations · 52 across the 12 of their papers we have counts for
5 papers · 1 filter
Learning Antidote Data to Individual Unfairness
Peizhao Li, Ethan Xia, Hongfu Liu
Fairness is essential for machine learning systems deployed in high-stake applications. Among all fairness notions, individual fairness, deriving from a consensus that `similar ind…
Characterizing the Influence of Graph Elements
Zizhang Chen, Peizhao Li, Hongfu Liu +1
Influence function, a method from robust statistics, measures the changes of model parameters or some functions about model parameters concerning the removal or modification of tra…
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
Anshuman Chhabra, Peizhao Li, Prasant Mohapatra +1
Clustering algorithms are widely used in many societal resource allocation applications, such as loan approvals and candidate recruitment, among others, and hence, biased or unfair…
Achieving Fairness at No Utility Cost via Data Reweighing with Influence
Peizhao Li, Hongfu Liu
With the fast development of algorithmic governance, fairness has become a compulsory property for machine learning models to suppress unintentional discrimination. In this paper,…
Deep Clustering based Fair Outlier Detection
Hanyu Song, Peizhao Li, Hongfu Liu
In this paper, we focus on the fairness issues regarding unsupervised outlier detection. Traditional algorithms, without a specific design for algorithmic fairness, could implicitl…