… that are rooted in the idea of equitable representation. To that end, we introduce different
ways to measure the cost of group representation. We can then define a fair clustering. …

… of fair k-median where each cluster is required to have a fair representation of individuals
from different groups. … We also study exact fair representation clustering, which is an important …

… fair representation. More precisely, these parameters define the maximum over- and minimum
under-representation of any group in any cluster… of our fair clustering algorithm changes …

… We consider fair clustering in the context of deep representation learning for large-scale
and highdimensional visual learning. To the best of our knowledge, this important setting has …

… fair representation can be used to guarantee fairness for unknown tasks and for multiple
fairness notions. We consider seven group … LFR [57] is a clustering-based method. Beutel et al. […

… clustering assignments that are guaranteed to be fair. In this paper, we propose a novel deep
fair clustering … clustering network and learn a representation that naturally yields compact …

… We study the question of fair clustering under the disparate impact doctrine, where each …
approximately equal representation in every cluster. We formulate the fair clustering problem …

… in the data, where data groups are defined via a set of protected attributes, such as …
notion for fair clustering based on diversity constraints on the set of selected cluster centers. …

… fair classification that achieves both group fairness (the proportion of members in a protected
group … We formulate fairness as an optimization problem of finding a good representation of …

… representation fairness notions in clustering: (1) Group … specific group fairness constraints
(1) GF: The group fair clustering (GF) of Chierichetti et al. [17] which roughly states that clusters …