# Diverse Data Selection under Fairness Constraints

arXiv: https://arxiv.org/abs/2010.09141 · HTML: https://mlsanity.com/paper/2010.09141/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [AD-LLM: Benchmarking Large Language Models for Anomaly Detection](https://mlsanity.com/paper/2412.11142/) (2024)
- [Core-sets for Fair and Diverse Data Summarization](https://mlsanity.com/paper/2310.08122/) (2023)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [Streaming Algorithms for Diversity Maximization with Fairness Constraints](https://mlsanity.com/paper/2208.00194/) (2022)
- [Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms](https://mlsanity.com/paper/2301.02053/) (2023)
- [GIST: Greedy Independent Set Thresholding for Max-Min Diversification with Submodular Utility](https://mlsanity.com/paper/2405.18754/) (2024)
- [Ordered Semantically Diverse Sampling for Textual Data](https://mlsanity.com/paper/2503.10698/) (2025)

## Abstract (arXiv)

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 offering fairness guarantees. In particular, we offer the first study that augments the Max-Min diversification objective with fairness constraints. More specifically, given a universe U of n elements that can be partitioned into m disjoint groups, we aim to retrieve a k-sized subset that maximizes the pairwise minimum distance within the set (diversity) and contains a pre-specified ki number of elements from each group i (fairness). We show that this problem is NP-complete even in metric spaces, and we propose three novel algorithms, linear in n, that provide strong theoretical approximation guarantees for different values of m and k. Finally, we extend our algorithms and analysis to the case where groups can be overlapping.