Making clusterings fairer by post-processing: algorithms, complexity results and experiments

• Published in: Data mining and knowledge discovery Vol. 37; no. 4; pp. 1404 - 1440
• Main Authors: Davidson, Ian, Bai, Zilong, Tran, Cindy Mylinh, Ravi, S. S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Clustering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Information Storage and Retrieval; Integer programming; Linear programming; Mathematical analysis; Physics; Special Issue on Bias and Fairness; Statistics for Engineering; Protected status; Algorithms; Clustering; Fairness; Complexity

While existing fairness work typically focuses on fair-by-design algorithms, here we consider making a fairness-unaware algorithm’s output fairer. Specifically, we explore the area of fairness in clustering by modifying clusterings produced by existing algorithms to make them fairer whilst retaining...