Add to ORKGAs algorithms play a large role in our decision making, the possibility of algorithmic bias has led researchers to explore the realm of fair algorithms. In this thesis, we explore the design of a fair algorithm for clustering a problem in unsupervised machine learning algorithm. Our algorithm aims to balance the representation of an arbitrary number of protected groups in each cluster. We extend prior work by allowing the points to belong to multiple protected groups and for users to compromise between stricter fairness and the clustering objective. We provide experimental validation of our work on the k-median, k-means and k-center objectives
Producción CientíficaWe consider the problem of diversity enhancing clustering, i.e, developing clus...
We study fair clustering problems as proposed by Chierichetti et al. [CKLV17]. Here, points hav...
Hierarchical Agglomerative Clustering (HAC) algorithms are extensively utilized in modern data scien...
As algorithms play a large role in our decision making, the possibility of algorithmic bias has led ...
Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that feature ubiq...
We study the question of fair clustering under the disparate impact doctrine, where each protected c...
We explore the area of fairness in clustering from the different perspective of modifying clustering...
In the last few years, the need of preventing classification biases due to race, gender, social stat...
Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing un...
Clustering problems and clustering algorithms are often overly sensitive to the presence of outliers...
The study of algorithmic fairness received growing attention recently. This stems from the awareness...
Unsupervised learning is widely recognized as one of the most important challenges facing machine le...
Algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and ...
We examine whether the quality of dierent clustering algorithms can be compared by a general, scient...
Clustering is a usual unsupervised machine learning technique for grouping the data points into grou...
Producción CientíficaWe consider the problem of diversity enhancing clustering, i.e, developing clus...
We study fair clustering problems as proposed by Chierichetti et al. [CKLV17]. Here, points hav...
Hierarchical Agglomerative Clustering (HAC) algorithms are extensively utilized in modern data scien...
As algorithms play a large role in our decision making, the possibility of algorithmic bias has led ...
Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that feature ubiq...
We study the question of fair clustering under the disparate impact doctrine, where each protected c...
We explore the area of fairness in clustering from the different perspective of modifying clustering...
In the last few years, the need of preventing classification biases due to race, gender, social stat...
Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing un...
Clustering problems and clustering algorithms are often overly sensitive to the presence of outliers...
The study of algorithmic fairness received growing attention recently. This stems from the awareness...
Unsupervised learning is widely recognized as one of the most important challenges facing machine le...
Algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and ...
We examine whether the quality of dierent clustering algorithms can be compared by a general, scient...
Clustering is a usual unsupervised machine learning technique for grouping the data points into grou...
Producción CientíficaWe consider the problem of diversity enhancing clustering, i.e, developing clus...
We study fair clustering problems as proposed by Chierichetti et al. [CKLV17]. Here, points hav...
Hierarchical Agglomerative Clustering (HAC) algorithms are extensively utilized in modern data scien...