# Fair Active Learning

arXiv: https://arxiv.org/abs/2001.01796 · HTML: https://mlsanity.com/paper/2001.01796/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2020.

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

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Adaptive Sampling for Minimax Fair Classification](https://mlsanity.com/paper/2103.00755/) (2021)
- [Adaptive Sampling Strategies to Construct Equitable Training Datasets](https://mlsanity.com/paper/2202.01327/) (2022)
- [Equal Opportunity of Coverage in Fair Regression](https://mlsanity.com/paper/2311.02243/) (2023)
- [Can Active Learning Preemptively Mitigate Fairness Issues?](https://mlsanity.com/paper/2104.06879/) (2021)
- [Fairness Without Harm: An Influence-Guided Active Sampling Approach](https://mlsanity.com/paper/2402.12789/) (2024)
- [Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias](https://mlsanity.com/paper/2110.10389/) (2021)
- [FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering](https://mlsanity.com/paper/2209.12756/) (2022)
- [Falcon: Fair Active Learning using Multi-armed Bandits](https://mlsanity.com/paper/2401.12722/) (2024)
- [DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement](https://mlsanity.com/paper/2107.05978/) (2021)
- [An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets](https://mlsanity.com/paper/2306.08954/) (2023)
- [More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias](https://mlsanity.com/paper/2207.07723/) (2022)
- [Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms](https://mlsanity.com/paper/2204.06664/) (2022)
- [Mitigating Algorithmic Bias with Limited Annotations](https://mlsanity.com/paper/2207.10018/) (2022)
- [Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play](https://mlsanity.com/paper/2302.05807/) (2023)
- [Survey of Federated Learning Models for Spatial-Temporal Mobility Applications](https://mlsanity.com/paper/2305.05257/) (2023)
- [ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP](https://mlsanity.com/paper/2308.02537/) (2023)
- [Fair Active Learning in Low-Data Regimes](https://mlsanity.com/paper/2312.08559/) (2023)
- [Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition Tasks](https://mlsanity.com/paper/2407.03895/) (2024)
- [Fair Robust Active Learning by Joint Inconsistency](https://mlsanity.com/paper/2209.10729/) (2022)
- [Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework](https://mlsanity.com/paper/2307.11379/) (2023)
- [Adaptive Boosting with Fairness-aware Reweighting Technique for Fair Classification](https://mlsanity.com/paper/2401.03097/) (2024)
- [Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education](https://mlsanity.com/paper/2109.07908/) (2021)
- [Fair Active Learning: Solving the Labeling Problem in Insurance](https://mlsanity.com/paper/2112.09466/) (2021)
- [An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem](https://mlsanity.com/paper/2302.02033/) (2023)
- [Benchmarking Multi-Domain Active Learning on Image Classification](https://mlsanity.com/paper/2312.00364/) (2023)
- [Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction](https://mlsanity.com/paper/2407.14210/) (2024)
- [Fair Class-Incremental Learning using Sample Weighting](https://mlsanity.com/paper/2410.01324/) (2024)
- [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)

## Abstract (arXiv)

Machine learning (ML) is increasingly being used in high-stakes applications impacting society. Therefore, it is of critical importance that ML models do not propagate discrimination. Collecting accurate labeled data in societal applications is challenging and costly. Active learning is a promising approach to build an accurate classifier by interactively querying an oracle within a labeling budget. We design algorithms for fair active learning that carefully selects data points to be labeled so as to balance model accuracy and fairness. We demonstrate the effectiveness and efficiency of our proposed algorithms over widely used benchmark datasets using demographic parity and equalized odds notions of fairness.