# Learning to Generate Fair Clusters from Demonstrations

arXiv: https://arxiv.org/abs/2102.03977 · HTML: https://mlsanity.com/paper/2102.03977/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [Fairness Testing: Testing Software for Discrimination](https://mlsanity.com/paper/1709.03221/) (2017)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Ranking with Fairness Constraints](https://mlsanity.com/paper/1704.06840/) (2017)
- [Fair k-Center Clustering for Data Summarization](https://mlsanity.com/paper/1901.08628/) (2019)
- [Online Learning with an Unknown Fairness Metric](https://mlsanity.com/paper/1802.06936/) (2018)
- [Individual Fairness for k-Clustering](https://mlsanity.com/paper/2002.06742/) (2020)
- [Metric Learning for Individual Fairness](https://mlsanity.com/paper/1906.00250/) (2019)
- [Clustering without Over-Representation](https://mlsanity.com/paper/1905.12753/) (2019)
- [Clustering with Noisy Queries](https://mlsanity.com/paper/1706.07510/) (2017)
- [Clustering with Same-Cluster Queries](https://mlsanity.com/paper/1606.02404/) (2016)
- [Fair Correlation Clustering](https://mlsanity.com/paper/2002.02274/) (2020)
- [On the price of explainability for some clustering problems](https://mlsanity.com/paper/2101.01576/) (2021)
- [Query Complexity of Clustering with Side Information](https://mlsanity.com/paper/1706.07719/) (2017)
- [Learning Representations by Humans, for Humans](https://mlsanity.com/paper/1905.12686/) (2019)
- [Distributional Individual Fairness in Clustering](https://mlsanity.com/paper/2006.12589/) (2020)
- [Balancing the Tradeoff Between Clustering Value and Interpretability](https://mlsanity.com/paper/1912.07820/) (2019)
- [Faster Balanced Clusterings in High Dimension](https://mlsanity.com/paper/1809.00932/) (2018)
- [Lexicographically Ordered Multi-Objective Clustering](https://mlsanity.com/paper/1903.00750/) (2019)

## Built on by (most cited first, 4 of 4)

- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021)
- [Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations](https://mlsanity.com/paper/2305.20020/) (2023)
- [Metric k-clustering using only Weak Comparison Oracles](https://mlsanity.com/paper/2601.19333/) (2026)

## Abstract (arXiv)

Fair clustering is the process of grouping similar entities together, while satisfying a mathematically well-defined fairness metric as a constraint. Due to the practical challenges in precise model specification, the prescribed fairness constraints are often incomplete and act as proxies to the intended fairness requirement, leading to biased outcomes when the system is deployed. We examine how to identify the intended fairness constraint for a problem based on limited demonstrations from an expert. Each demonstration is a clustering over a subset of the data. We present an algorithm to identify the fairness metric from demonstrations and generate clusters using existing off-the-shelf clustering techniques, and analyze its theoretical properties. To extend our approach to novel fairness metrics for which clustering algorithms do not currently exist, we present a greedy method for clustering. Additionally, we investigate how to generate interpretable solutions using our approach. Empirical evaluation on three real-world datasets demonstrates the effectiveness of our approach in quickly identifying the underlying fairness and interpretability constraints, which are then used to generate fair and interpretable clusters.