Bias Mitigation papers, 2023–2026

Key findings

• Bias Mitigation went from 50 papers in 2023 to 76 in 2026, roughly 1.5x in three years.
• ICLR publishes the most Bias Mitigation work: 85 papers from 2023 to 2026, 30% of the four-venue total.
• The fastest-growing overlap is with Multimodal LLMs: papers tagged with both rose from 0 in 2023 to 15 in 2026.
• Among the 5 topics under Causal and Fairness, it ranks #4 by 2026 paper count; the fastest grower in the group, Causal Discovery, is up 2.4x versus 1.5x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Controlling for Omitted Variable Bias in Deep Neural Networks
• ICML 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
• ICLR 2026Bi-directional Bias Attribution: Debiasing Large Language Models without Modifying Prompts
• CVPR 2026BAMI Training-Free Bias Mitigation in GUI Grounding
• NeurIPS 2025Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector
• ICML 2025Clipped SGD Algorithms for Performative Prediction: Tight Bounds for Stochastic Bias and Remedies
• ICLR 2025ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition
• CVPR 2025Data Distributional Properties As Inductive Bias for Systematic Generalization
• NeurIPS 2024Bayesian Identification of the Hamiltonian Inductive Bias in Dynamical Systems
• ICML 2024Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
• ICLR 2024A 2-Dimensional State Space Layer for Spatial Inductive Bias
• CVPR 2024Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation
• NeurIPS 2023Fast Model DeBias with Machine Unlearning
• ICML 2023FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
• ICLR 2023Bias Propagation in Federated Learning
• CVPR 2023Bias Mimicking: A Simple Sampling Approach for Bias Mitigation
• NeurIPS 2026Empirical Bayes Rebiasing
• ICML 2026DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation
• ICLR 2026BiasFreeBench: a Benchmark for Mitigating Bias in Large Language Model Responses
• CVPR 2026Bias at the End of the Score

All Bias Mitigation papers

73 papers, page 1 of 2

1. CVPR 2024BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model
2. CVPR 2024Balancing Act: Distribution-Guided Debiasing in Diffusion Models
3. CVPR 2024Batch Normalization Alleviates the Spectral Bias in Coordinate Networks
4. CVPR 2024Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation
5. CVPR 2024Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair
6. CVPR 2024Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain
7. CVPR 2024FINER: Flexible Spectral-bias Tuning in Implicit NEural Representation by Variable-periodic Activation Functions
8. CVPR 2024Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincare Ball
9. CVPR 2024Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation
10. CVPR 2024On the Content Bias in Frechet Video Distance
11. CVPR 2024Robust Emotion Recognition in Context Debiasing
12. CVPR 2024Spatio-Temporal Turbulence Mitigation: A Translational Perspective
13. CVPR 2024Would Deep Generative Models Amplify Bias in Future Models?
14. ICLR 2024A 2-Dimensional State Space Layer for Spatial Inductive Bias
15. ICLR 2024Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
16. ICLR 2024Debiasing Algorithm through Model Adaptation
17. ICLR 2024Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
18. ICLR 2024Elucidating the Exposure Bias in Diffusion Models
19. ICLR 2024FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
20. ICLR 2024Implicit bias of SGD in $L_2$-regularized linear DNNs: One-way jumps from high to low rank
21. ICLR 2024Leveraging Uncertainty Estimates To Improve Classifier Performance
22. ICLR 2024On Bias-Variance Alignment in Deep Models
23. ICLR 2024On the Foundations of Shortcut Learning
24. ICLR 2024Post-hoc bias scoring is optimal for fair classification
25. ICLR 2024Procedural Fairness Through Decoupling Objectionable Data Generating Components
26. ICML 2024A Universal Class of Sharpness-Aware Minimization Algorithms
27. ICML 2024Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate
28. ICML 2024Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning
29. ICML 2024Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
30. ICML 2024Can Implicit Bias Imply Adversarial Robustness?
31. ICML 2024Convergence of Some Convex Message Passing Algorithms to a Fixed Point
32. ICML 2024Differentially Private Bias-Term Fine-tuning of Foundation Models
33. ICML 2024Discovering Bias in Latent Space: An Unsupervised Debiasing Approach
34. ICML 2024Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning
35. ICML 2024Evaluating Model Bias Requires Characterizing its Mistakes
36. ICML 2024Feature Importance Disparities for Data Bias Investigations
37. ICML 2024How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers
38. ICML 2024In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
39. ICML 2024Initial Guessing Bias: How Untrained Networks Favor Some Classes
40. ICML 2024Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters
41. ICML 2024Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias
42. ICML 2024On the Implicit Bias of Adam
43. ICML 2024On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions
44. ICML 2024Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data
45. ICML 2024Simplicity Bias via Global Convergence of Sharpness Minimization
46. ICML 2024Statistical Inference Under Constrained Selection Bias
47. ICML 2024The Role of Learning Algorithms in Collective Action
48. ICML 2024Whispering Experts: Neural Interventions for Toxicity Mitigation in Language Models
49. NeurIPS 2024A distributional simplicity bias in the learning dynamics of transformers
50. NeurIPS 2024Addressing Bias in Online Selection with Limited Budget of Comparisons

FAQ

How many Bias Mitigation papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

284 in total: 50 in 2023, 73 in 2024, 85 in 2025, 76 in 2026.

How many Bias Mitigation papers are at NeurIPS 2026?

NeurIPS 2026 has 14 Bias Mitigation papers, versus 38 the year before.

How many Bias Mitigation papers are at ICLR 2026?

ICLR 2026 has 24 Bias Mitigation papers, versus 23 the year before.

How many Bias Mitigation papers are at ICML 2026?

ICML 2026 has 24 Bias Mitigation papers, versus 17 the year before.

How many Bias Mitigation papers are at CVPR 2026?

CVPR 2026 has 14 Bias Mitigation papers, versus 7 the year before.

Which conference publishes the most Bias Mitigation papers?

ICLR, with 85 papers from 2023 to 2026.