mlsanityData to 14 Oct 2026

FairGrad: Fairness Aware Gradient Descent

arXiv 2206.10923 ↗First posted 22 Jun 2022cs.LG cs.AI cs.CYcited by 8

Not on the map. Why?

Abstract (arXiv)

We address the problem of group fairness in classification, where the objective is to learn models that do not unjustly discriminate against subgroups of the population. Most existing approaches are limited to simple binary tasks or involve difficult to implement training mechanisms which reduces their practical applicability. In this paper, we propose FairGrad, a method to enforce fairness based on a re-weighting scheme that iteratively learns group specific weights based on whether they are advantaged or not. FairGrad is easy to implement, accommodates various standard fairness definitions, and comes with minimal overhead. Furthermore, we show that it is competitive with standard baselines over various datasets including ones used in natural language processing and computer vision. FairGrad is available as a PyPI package at - https://pypi.org/project/fairgrad

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 31 of its 60 references that are in the map. First posted on arXiv 22 Jun 2022.

Builds on

31 of 60 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  4. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  6. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  7. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  8. 2017Counterfactual Fairnesscited by 804
  9. 2014Certifying and removing disparate impactcited by 715
  10. 2018A Reductions Approach to Fair Classificationcited by 587
  11. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 449
  12. 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 429
19 more
  1. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 384
  2. 2020Fairness in Machine Learning: A Surveycited by 370
  3. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 369
  4. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  5. 2016Demographic Dialectal Variation in Social Media: A Case Study of African-American Englishcited by 196
  6. 2018Adversarial Removal of Demographic Attributes from Text Datacited by 170
  7. 2019Identifying and Correcting Label Bias in Machine Learningcited by 146
  8. 2017Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasmcited by 118
  9. 2020FairBatch: Batch Selection for Model Fairnesscited by 99
  10. 2018Towards Robust and Privacy-preserving Text Representationscited by 75
  11. 2016Satisfying Real-world Goals with Dataset Constraintscited by 68
  12. 2019Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classificationcited by 60
  13. 2018Two-Player Games for Efficient Non-Convex Constrained Optimizationcited by 55
  14. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  15. 2021Diverse Adversaries for Mitigating Bias in Trainingcited by 48
  16. 2019AdaFair: Cumulative Fairness Adaptive Boostingcited by 45
  17. 2020FAE: A Fairness-Aware Ensemble Frameworkcited by 34
  18. 2018Gradient Reversal Against Discriminationcited by 20
  19. 2022Fair NLP Models with Differentially Private Text Encoderscited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 2; 2026: 1.

8 papers.

  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2022Differential Privacy has Bounded Impact on Fairness in Classificationcited by 18
  3. 2023Understanding Fairness Surrogate Functions in Algorithmic Fairnesscited by 8
  4. 2024Regression under demographic parity constraints via unlabeled post-processingcited by 5
  5. 2025Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognitioncited by 3
  6. 2025COPA: Comparing the incomparable in multi-objective model evaluationcited by 3
  7. 2026Uncovering Cross-Objective Interference in Multi-Objective Alignmentcited by 2
  8. 2024On the Impact of Output Perturbation on Fairness in Binary Linear Classificationcited by 0