mlsanityData to 7 Oct 2026

Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey

arXiv 2211.05617 ↗First posted 10 Nov 2022cs.LG cs.AI cs.CLcited by 22

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Abstract (arXiv)

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 154 of its 233 references that are in the map. First posted on arXiv 10 Nov 2022.

Builds on

154 of 233 references.

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Citing papers per year: 2024: 10; 2025: 6; 2026: 6.

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  1. 2024Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Promptscited by 15
  2. 2024Verifiable evaluations of machine learning models using zkSNARKscited by 14
  3. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  4. 2024Fairness and Bias Mitigation in Computer Vision: A Surveycited by 12
  5. 2024Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifoldscited by 6
  6. 2024SDFD: Building a Versatile Synthetic Face Image Dataset with Diverse Attributescited by 6
  7. 2025Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Modelscited by 6
  8. 2025A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimulicited by 6
  9. 2024Mitigating Bias Using Model-Agnostic Data Attributioncited by 3
  10. 2025Attention IoU: Examining Biases in CelebA using Attention Mapscited by 3
  11. 2025MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networkscited by 2
  12. 2026Inference-Time Machine Unlearning via Gated Activation Redirectioncited by 2
10 more
  1. 2024Social Science Is Necessary for Operationalizing Socially Responsible Foundation Modelscited by 1
  2. 2025Inference Time Debiasing Concepts in Diffusion Modelscited by 1
  3. 2026Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generationcited by 1
  4. 2026HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasingcited by 1
  5. 2024Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignmentcited by 0
  6. 2024TransFair: Transferring Fairness from Ocular Disease Classification to Progression Predictioncited by 0
  7. 2025A Framework for Benchmarking Fairness-Utility Trade-offs in Text-to-Image Models via Pareto Frontierscited by 0
  8. 2026Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forwardcited by 0
  9. 2026FairLLaVA: Fairness-Aware Parameter-Efficient Fine-Tuning for Large Vision-Language Assistantscited by 0
  10. 2026Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effectscited by 0