mlsanityData to 7 Oct 2026

Fairness in Deep Learning: A Computational Perspective

arXiv 1908.08843 ↗First posted 23 Aug 2019cs.LG cs.AI cs.CYcited by 123

Not on the map. Why?

Abstract (arXiv)

Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. However, deep learning models might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative impacts on individuals and society. Therefore, fairness in deep learning has attracted tremendous attention recently. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. Specifically, we show that interpretability can serve as a useful ingredient to diagnose the reasons that lead to algorithmic discrimination. We also discuss fairness mitigation approaches categorized according to three stages of deep learning life-cycle, aiming to push forward the area of fairness in deep learning and build genuinely fair and reliable deep learning systems.

Key facts. Cited by 123 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Aug 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 21; 2022: 34; 2023: 30; 2024: 23; 2025: 13; 2026: 2.

123 papers.

Sort:

By area:

  1. 2021A survey on datasets for fairness-aware machine learningcited by 157
  2. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  3. 2023A Survey on Fairness in Large Language Modelscited by 91
  4. 2022Shortcut Learning of Large Language Models in Natural Language Understandingcited by 83
  5. 2022Distilling Model Failures as Directions in Latent Spacecited by 82
  6. 2021Towards Measuring Fairness in AI: the Casual Conversations Datasetcited by 80
  7. 2022Fairness in Graph Mining: A Surveycited by 76
  8. 2023Fast Model Debias with Machine Unlearningcited by 70
  9. 2022The Effects of Regularization and Data Augmentation are Class Dependentcited by 67
  10. 2021Fairness via Representation Neutralizationcited by 63
  11. 2022Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakagecited by 61
  12. 2022FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosiscited by 59
38 more
  1. 2021Towards Fairness-Aware Federated Learningcited by 57
  2. 2021Shallow Bayesian Meta Learning for Real-World Few-Shot Recognitioncited by 48
  3. 2021NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identificationcited by 42
  4. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  5. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  6. 2021An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wildcited by 30
  7. 2022FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classificationcited by 30
  8. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  9. 2021Estimating and Improving Fairness with Adversarial Learningcited by 26
  10. 2023Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluationcited by 25
  11. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  12. 2022FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learningcited by 22
  13. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  14. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  15. 2022Interpreting Unfairness in Graph Neural Networks via Training Node Attributioncited by 18
  16. 2024Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Imagescited by 18
  17. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  18. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  19. 2021MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Datasetcited by 15
  20. 2023Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysiscited by 15
  21. 2024On Catastrophic Inheritance of Large Foundation Modelscited by 15
  22. 2023Efficient XAI Techniques: A Taxonomic Surveycited by 14
  23. 2023Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosiscited by 14
  24. 2021Towards Equity and Algorithmic Fairness in Student Grade Predictioncited by 13
  25. 2022Analyzing Fairness in Deepfake Detection With Massively Annotated Databasescited by 13
  26. 2022Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Modelcited by 12
  27. 2022Fair SA: Sensitivity Analysis for Fairness in Face Recognitioncited by 11
  28. 2022How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shiftscited by 11
  29. 2021Fairness-Aware Unsupervised Feature Selectioncited by 10
  30. 2021Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice modelscited by 10
  31. 2022Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networkscited by 10
  32. 2022Fairness and Bias in Robot Learningcited by 10
  33. 2024Achieve Fairness without Demographics for Dermatological Disease Diagnosiscited by 10
  34. 2021CASIA-Face-Africa: A Large-scale African Face Image Databasecited by 9
  35. 2021On the Fairness of Swarm Learning in Skin Lesion Classificationcited by 9
  36. 2023Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptationcited by 9
  37. 2022Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Biascited by 8
  38. 2022Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicinecited by 8

50 most cited of 123 All 123 (JSON)