mlsanityData to 7 Oct 2026

Resource-constrained Fairness

arXiv 2406.01290 ↗First posted 3 Jun 2024cs.LG cs.CYcited by 1

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Abstract (arXiv)

Access to resources strongly constrains the decisions we make. While we might wish to offer every student a scholarship, or schedule every patient for follow-up meetings with a specialist, limited resources mean that this is not possible. When deploying machine learning systems, these resource constraints are simply enforced by varying the threshold of a classifier. However, these finite resource limitations are disregarded by most existing tools for fair machine learning, which do not allow the specification of resource limitations and do not remain fair when varying thresholds. This makes them ill-suited for real-world deployment. Our research introduces the concept of "resource-constrained fairness" and quantifies the cost of fairness within this framework. We demonstrate that the level of available resources significantly influences this cost, a factor overlooked in previous evaluations.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 44 references that are in the map. First posted on arXiv 3 Jun 2024.

Builds on

23 of 44 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  4. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  5. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
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  7. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  8. 2019Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigationcited by 252
  9. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  10. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 184
  11. 2018Delayed Impact of Fair Machine Learningcited by 183
  12. 2021A survey on datasets for fairness-aware machine learningcited by 157
11 more
  1. 2020Minimax Pareto Fairness: A Multi Objective Perspectivecited by 152
  2. 2018Why Is My Classifier Discriminatory?cited by 143
  3. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  4. 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 66
  5. 2020Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policycited by 44
  6. 2023The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by defaultcited by 36
  7. 2018Fair Algorithms for Learning in Allocation Problemscited by 29
  8. 2021Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systemscited by 20
  9. 2019Fairness and Utilization in Allocating Resources with Uncertain Demandcited by 16
  10. 2022Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networkscited by 10
  11. 2022Repairing Regressors for Fair Binary Classification at Any Decision Thresholdcited by 8

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12