5 citations · 7 across the 4 of their papers we have counts for
4 papers
Group fairness without demographics using social networks
David Liu, Virginie Do, Nicolas Usunier +1
Group fairness is a popular approach to prevent unfavorable treatment of individuals based on sensitive attributes such as race, gender, and disability. However, the reliance of gr…
BiaScope: Visual Unfairness Diagnosis for Graph Embeddings
Agapi Rissaki, Bruno Scarone, David Liu +4
The issue of bias (i.e., systematic unfairness) in machine learning models has recently attracted the attention of both researchers and practitioners. For the graph mining communit…
Identifying and Mitigating Instability in Embeddings of the Degenerate Core
David Liu, Tina Eliassi-Rad
Are the embeddings of a graph's degenerate core stable? What happens to the embeddings of nodes in the degenerate core as we systematically remove periphery nodes (by repeated peel…
RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity
David Liu, Zohair Shafi, William Fleisher +2
We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO). RAWLSNET's BN models generate aspir…