mlsanityData to 7 Oct 2026

Fair Active Learning

arXiv 2001.01796 ↗First posted 6 Jan 2020cs.LG stat.MLcited by 30

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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.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 7; 2023: 9; 2024: 6; 2025: 0; 2026: 2.

30 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021Adaptive Sampling for Minimax Fair Classificationcited by 24
  3. 2022Adaptive Sampling Strategies to Construct Equitable Training Datasetscited by 18
  4. 2023Equal Opportunity of Coverage in Fair Regressioncited by 16
  5. 2021Can Active Learning Preemptively Mitigate Fairness Issues?cited by 15
  6. 2024Fairness Without Harm: An Influence-Guided Active Sampling Approachcited by 12
  7. 2021Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Biascited by 11
  8. 2022FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clusteringcited by 6
  9. 2024Falcon: Fair Active Learning using Multi-armed Banditscited by 6
  10. 2021DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinementcited by 4
  11. 2023An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasetscited by 4
  12. 2022More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Biascited by 3
18 more
  1. 2022Achieving Representative Data via Convex Hull Feasibility Sampling Algorithmscited by 2
  2. 2022Mitigating Algorithmic Bias with Limited Annotationscited by 2
  3. 2023Pushing the Accuracy-Group Robustness Frontier with Introspective Self-playcited by 2
  4. 2023Survey of Federated Learning Models for Spatial-Temporal Mobility Applicationscited by 2
  5. 2023ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLPcited by 2
  6. 2023Fair Active Learning in Low-Data Regimescited by 2
  7. 2024Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition Taskscited by 2
  8. 2022Fair Robust Active Learning by Joint Inconsistencycited by 1
  9. 2023Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Frameworkcited by 1
  10. 2024Adaptive Boosting with Fairness-aware Reweighting Technique for Fair Classificationcited by 1
  11. 2021Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Educationcited by 0
  12. 2021Fair Active Learning: Solving the Labeling Problem in Insurancecited by 0
  13. 2023An Asymptotically Optimal Algorithm for the Convex Hull Membership Problemcited by 0
  14. 2023Benchmarking Multi-Domain Active Learning on Image Classificationcited by 0
  15. 2024Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reductioncited by 0
  16. 2024Fair Class-Incremental Learning using Sample Weightingcited by 0
  17. 2026Exploring Entropy-based Active Learning for Fair Brain Segmentationcited by 0
  18. 2026FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Datacited by 0