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Found 4 papers, 2 papers with code
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability.
Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
To balance the data distribution between the majority and the minority groups, our approach deemphasizes samples from the majority group.
Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution Discrepancy
This paper presents MASC, a data augmentation approach that leverages affinity clustering to balance the representation of non-protected and protected groups of a target dataset by utilizing instances of the same protected attributes from similar datasets that are categorized in the same cluster as the target dataset by sharing instances of the protected attribute.
Context matters for fairness -- a case study on the effect of spatial distribution shifts
With the ever growing involvement of data-driven AI-based decision making technologies in our daily social lives, the fairness of these systems is becoming a crucial phenomenon.
