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Found 5 papers, 2 papers with code
Fair Active Ranking from Pairwise Preferences
Our proposed objective function asks to minimize the $\ell_q$ norm of the error of the groups, where the error of a group is the $\ell_p$ norm of the error of all the items within that group, for $p, q \geq 1$.
Improved Approximation for Fair Correlation Clustering
Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing unfairness is a major challenge.
Fair Hierarchical Clustering
As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair.
Fair Correlation Clustering
We define a fairlet decomposition with cost similar to the $k$-median cost and this allows us to obtain approximation algorithms for a wide range of fairness constraints.
Clustering without Over-Representation
In this paper we consider clustering problems in which each point is endowed with a color.
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