# Fair and Diverse DPP-based Data Summarization

arXiv: https://arxiv.org/abs/1802.04023 · HTML: https://mlsanity.com/paper/1802.04023/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

## Built on by (most cited first, 30 of 39)

- [Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs](https://mlsanity.com/paper/2506.10967/) (2025)
- [Data Distillation: A Survey](https://mlsanity.com/paper/2301.04272/) (2023)
- [MDP3: A Training-free Approach for List-wise Frame Selection in Video-LLMs](https://mlsanity.com/paper/2501.02885/) (2025)
- [CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?](https://mlsanity.com/paper/2403.04547/) (2024)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning](https://mlsanity.com/paper/2511.05489/) (2025)
- [Black-Box Testing of Deep Neural Networks Through Test Case Diversity](https://mlsanity.com/paper/2112.12591/) (2021)
- [Fair Abstractive Summarization of Diverse Perspectives](https://mlsanity.com/paper/2311.07884/) (2023)
- [Mind the Graph When Balancing Data for Fairness or Robustness](https://mlsanity.com/paper/2406.17433/) (2024)
- [Coresets for Time Series Clustering](https://mlsanity.com/paper/2110.15263/) (2021)
- [On Measuring Fairness in Generative Models](https://mlsanity.com/paper/2310.19297/) (2023)
- [Pairwise Fairness for Ordinal Regression](https://mlsanity.com/paper/2105.03153/) (2021)
- [Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems](https://mlsanity.com/paper/2204.05158/) (2022)
- [Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images](https://mlsanity.com/paper/2401.14322/) (2024)
- [Fair Submodular Cover](https://mlsanity.com/paper/2407.04804/) (2024)
- [Group Equality in Adaptive Submodular Maximization](https://mlsanity.com/paper/2207.03364/) (2022)
- [Fast Feature Selection with Fairness Constraints](https://mlsanity.com/paper/2202.13718/) (2022)
- [Group Fairness in Non-monotone Submodular Maximization](https://mlsanity.com/paper/2302.01546/) (2023)
- [Core-sets for Fair and Diverse Data Summarization](https://mlsanity.com/paper/2310.08122/) (2023)
- [Quality-Diversity Generative Sampling for Learning with Synthetic Data](https://mlsanity.com/paper/2312.14369/) (2023)
- [Streaming Algorithms for Diversity Maximization with Fairness Constraints](https://mlsanity.com/paper/2208.00194/) (2022)
- [Fairness in Streaming Submodular Maximization over a Matroid Constraint](https://mlsanity.com/paper/2305.15118/) (2023)
- [TADACap: Time-series Adaptive Domain-Aware Captioning](https://mlsanity.com/paper/2504.11441/) (2025)
- [Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information](https://mlsanity.com/paper/2206.01326/) (2022)
- [Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms](https://mlsanity.com/paper/2301.02053/) (2023)
- [Designing Data: Proactive Data Collection and Iteration for Machine Learning](https://mlsanity.com/paper/2301.10319/) (2023)
- [Achieving Long-term Fairness in Submodular Maximization through Randomization](https://mlsanity.com/paper/2304.04700/) (2023)
- [Beyond Submodularity: A Unified Framework of Randomized Set Selection with Group Fairness Constraints](https://mlsanity.com/paper/2304.06596/) (2023)
- [Fairness in Submodular Maximization over a Matroid Constraint](https://mlsanity.com/paper/2312.14299/) (2023)
- [Multi-Group Proportional Representation in Retrieval](https://mlsanity.com/paper/2407.08571/) (2024)

## Abstract (arXiv)

Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization. However, recent studies have noted the occurrence of bias (under- or over-representation of a certain gender or race) in such data summarization methods. In this paper we initiate a study of the problem of outputting a diverse and fair summary of a given dataset. We work with a well-studied determinantal measure of diversity and corresponding distributions (DPPs) and present a framework that allows us to incorporate a general class of fairness constraints into such distributions. Coming up with efficient algorithms to sample from these constrained determinantal distributions, however, suffers from a complexity barrier and we present a fast sampler that is provably good when the input vectors satisfy a natural property. Our experimental results on a real-world and an image dataset show that the diversity of the samples produced by adding fairness constraints is not too far from the unconstrained case, and we also provide a theoretical explanation of it.