An overview of fairness in clustering
Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …
feature ubiquitously in modern data science, and play a key role in many learning-based …
A review of clustering models in educational data science toward fairness-aware learning
Ensuring fair access to quality education is essential for every education system to fully
realize every student's potential. Nowadays, machine learning (ML) is transforming …
realize every student's potential. Nowadays, machine learning (ML) is transforming …
Algorithmic fairness datasets: the story so far
Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …
decisions, directly impacting people's well-being. As a result, a growing community of …
Improved approximation algorithms for individually fair clustering
A Vakilian, M Yalciner - International conference on artificial …, 2022 - proceedings.mlr.press
We consider the $ k $-clustering problem with $\ell_p $-norm cost, which includes $ k $-
median, $ k $-means and $ k $-center, under an individual notion of fairness proposed by …
median, $ k $-means and $ k $-center, under an individual notion of fairness proposed by …
Approximation algorithms for socially fair clustering
Y Makarychev, A Vakilian - Conference on Learning Theory, 2021 - proceedings.mlr.press
We present an (e^{O (p)}(log\ell)/(log log\ell))-approximation algorithm for socially fair
clustering with the l_p-objective. In this problem, we are given a set of points in a metric …
clustering with the l_p-objective. In this problem, we are given a set of points in a metric …
Approximation algorithms for fair range clustering
SS Hotegni, S Mahabadi… - … Conference on Machine …, 2023 - proceedings.mlr.press
This paper studies the fair range clustering problem in which the data points are from
different demographic groups and the goal is to pick $ k $ centers with the minimum …
different demographic groups and the goal is to pick $ k $ centers with the minimum …
Fair hierarchical clustering
As machine learning has become more prevalent, researchers have begun to recognize the
necessity of ensuring machine learning systems are fair. Recently, there has been an …
necessity of ensuring machine learning systems are fair. Recently, there has been an …
Fair and fast k-center clustering for data summarization
We consider two key issues faced by many clustering methods when used for data
summarization, namely (a) an unfair representation of" demographic groups” and (b) …
summarization, namely (a) an unfair representation of" demographic groups” and (b) …
Tackling documentation debt: a survey on algorithmic fairness datasets
A growing community of researchers has been investigating the equity of algorithms,
advancing the understanding of risks and opportunities of automated decision-making for …
advancing the understanding of risks and opportunities of automated decision-making for …
Diverse data selection under fairness constraints
Z Moumoulidou, A McGregor, A Meliou - arXiv preprint arXiv:2010.09141, 2020 - arxiv.org
Diversity is an important principle in data selection and summarization, facility location, and
recommendation systems. Our work focuses on maximizing diversity in data selection, while …
recommendation systems. Our work focuses on maximizing diversity in data selection, while …