# FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering

arXiv: https://arxiv.org/abs/2209.12756 · HTML: https://mlsanity.com/paper/2209.12756/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 21 Sep 2022.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Minimax Pareto Fairness: A Multi Objective Perspective](https://mlsanity.com/paper/2011.01821/) (2020)
- [Inherent Tradeoffs in Learning Fair Representations](https://mlsanity.com/paper/1906.08386/) (2019)
- [Learning Active Learning from Data](https://mlsanity.com/paper/1703.03365/) (2017)
- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [Active Learning for Domain Adaptation: An Energy-Based Approach](https://mlsanity.com/paper/2112.01406/) (2021)
- [Individual Fairness in Hindsight](https://mlsanity.com/paper/1812.04069/) (2018)
- [Improved Approximation Algorithms for Individually Fair Clustering](https://mlsanity.com/paper/2106.14043/) (2021)
- [Fair Active Learning](https://mlsanity.com/paper/2001.01796/) (2020)
- [Fair Correlation Clustering](https://mlsanity.com/paper/2002.02274/) (2020)
- [Fairness in Clustering with Multiple Sensitive Attributes](https://mlsanity.com/paper/1910.05113/) (2019)
- [A Notion of Individual Fairness for Clustering](https://mlsanity.com/paper/2006.04960/) (2020)
- [Deep Active Learning with Adaptive Acquisition](https://mlsanity.com/paper/1906.11471/) (2019)
- [Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints](https://mlsanity.com/paper/2103.02013/) (2021)
- [Fairness With Minimal Harm: A Pareto-Optimal Approach For Healthcare](https://mlsanity.com/paper/1911.06935/) (2019)
- [Survey on Fair Reinforcement Learning: Theory and Practice](https://mlsanity.com/paper/2205.10032/) (2022)
- [Deep Bayesian Active Learning, A Brief Survey on Recent Advances](https://mlsanity.com/paper/2012.08044/) (2020)

## Built on by (most cited first, 6 of 6)

- [Fair Active Learning in Low-Data Regimes](https://mlsanity.com/paper/2312.08559/) (2023)
- [Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond](https://mlsanity.com/paper/2507.08866/) (2025)
- [FILM: Framework for Imbalanced Learning Machines based on a new unbiased performance measure and a new ensemble-based technique](https://mlsanity.com/paper/2503.04370/) (2025)
- [Exploring Entropy-based Active Learning for Fair Brain Segmentation](https://mlsanity.com/paper/2605.01706/) (2026)
- [FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data](https://mlsanity.com/paper/2606.23515/) (2026)
- [Provenance Guided Incremental Learning Under Evolving Concept Definitions](https://mlsanity.com/paper/2608.23893/) (2026)

## Abstract (arXiv)

Active Learning (AL) techniques have proven to be highly effective in reducing data labeling costs across a range of machine learning tasks. Nevertheless, one known challenge of these methods is their potential to introduce unfairness towards sensitive attributes. Although recent approaches have focused on enhancing fairness in AL, they tend to reduce the model's accuracy. To address this issue, we propose a novel strategy, named Fair Active Learning using fair Clustering, Uncertainty, and Representativeness (FAL-CUR), to improve fairness in AL. FAL-CUR tackles the fairness problem in AL by combining fair clustering with an acquisition function that determines which samples to query based on their uncertainty and representativeness scores. We evaluate the performance of FAL-CUR on four real-world datasets, and the results demonstrate that FAL-CUR achieves a 15% - 20% improvement in fairness compared to the best state-of-the-art method in terms of equalized odds while maintaining stable accuracy scores. Furthermore, an ablation study highlights the crucial roles of fair clustering in preserving fairness and the acquisition function in stabilizing the accuracy performance.