arXiv:2606. 04777v1 Announce Type: new Abstract: Clustering is increasingly used to support high-impact decisions, yet standard objectives such as $k$-means can produce clusterings that treat demographic groups unequally.
By Antonia Karra, Vasiliki Papanikou, Georgios Vardakas, Evaggelia Pitoura, Aristidis Likas
arXiv:2608.30223v1 Announce Type: cross
Abstract: We propose a new design of fair classifiers for multi-class classification problems in the presence of vector-valued sensitive attributes. In that sc...
By Darinka Dentcheva, Xiangyu Tian
arXiv:2602.08589v2 Announce Type: replace
Abstract: PageRank (PR) is a fundamental algorithm in graph machine learning tasks. Owing to the increasing importance of algorithmic fairness, we consider t...
By Emmanouil Kariotakis, Aritra Konar
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
By Bruno Scarone, Alfredo Viola, Ren\'ee J. Miller
arXiv:2304. 13917v4 Announce Type: replace Abstract: In recent years, there has been a surge in effort to formalize notions of fairness in machine learning.
By Haris Aziz, Barton E. Lee, Sean Morota Chu, Jeremy Vollen
We propose a new design of fair classifiers for multi-class classification problems in the presence of vector-valued sensitive attributes. In that scenario each sensitive attribute has multiple values...