Social aspects · fairness papers, 2023–2026

Key findings

• Social aspects · fairness went from 36 papers in 2023 to 111 in 2026, roughly 3.1x in three years.
• NeurIPS publishes the most Social aspects · fairness work: 180 papers from 2023 to 2026, 50% of the four-venue total.
• The fastest-growing overlap is with Social aspects · safety: papers tagged with both rose from 1 in 2023 to 56 in 2026.
• Among the 5 topics under Alignment and Responsible AI, it ranks #5 by 2026 paper count; the fastest grower in the group, Social aspects · safety, is up 93x versus 3.1x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Beyond Feature Disruption: Boundary-Diverting Unlearnable Examples against Linear Probing
• ICML 2026A Game-Theoretic Framework for Measuring and Explaining Metric Compatibility in Fair Machine Learning
• ICLR 2026Bi-directional Bias Attribution: Debiasing Large Language Models without Modifying Prompts
• CVPR 2026Omni-MMSI Toward Identity-attributed Social Interaction Understanding
• NeurIPS 2025Discretization-free Multicalibration through Loss Minimization over Tree Ensembles
• ICML 2025Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
• ICLR 2025Prompting Fairness: Integrating Causality to Debias Large Language Models
• CVPR 2025Revisiting Fairness in Multitask Learning: A Performance-Driven Approach for Variance Reduction
• NeurIPS 2024A Closer Look at AUROC and AUPRC under Class Imbalance
• ICML 2024Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized
• ICLR 2024Procedural Fairness Through Decoupling Objectionable Data Generating Components
• CVPR 2024Preserving Fairness Generalization in Deepfake Detection
• NeurIPS 2023[Re] Fairness Guarantees under Demographic Shift
• ICML 2023Fairness in Streaming Submodular Maximization over a Matroid Constraint
• ICLR 2023Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes
• CVPR 2023Improving Fairness in Facial Albedo Estimation via Visual-Textual Cues
• NeurIPS 2026Chatter Attack: Resource Consumption Attack for Large Language Models
• ICML 2026Adaptive Generation of Bias-Eliciting Questions for LLMs
• ICLR 2026Bi-Criteria Metric Distortion
• CVPR 2026Decoupling Bias Aligning Distributions Synergistic Fairness Optimization for Deepfake Detection

All Social aspects · fairness papers

132 papers, page 2 of 3

51. ICML 2025Prediction via Shapley Value Regression
52. ICML 2025Preserving AUC Fairness in Learning with Noisy Protected Groups
53. ICML 2025Relative Error Fair Clustering in the Weak-Strong Oracle Model
54. ICML 2025Representative Ranking for Deliberation in the Public Sphere
55. ICML 2025Selective Preference Aggregation
56. ICML 2025Settling the Maximin Share Fairness for Scheduling among Groups of Machines
57. ICML 2025Statistical Collusion by Collectives on Learning Platforms
58. ICML 2025Subgroups Matter for Robust Bias Mitigation
59. ICML 2025Tensor Product Neural Networks for Functional ANOVA Model
60. ICML 2025The Disparate Benefits of Deep Ensembles
61. ICML 2025The Elicitation Game: Evaluating Capability Elicitation Techniques
62. ICML 2025The Illusion of Role Separation: Hidden Shortcuts in LLM Role Learning (and How to Fix Them)
63. ICML 2025The Value of Prediction in Identifying the Worst-Off
64. ICML 2025TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference
65. ICML 2025Towards Attributions of Input Variables in a Coalition
66. ICML 2025Two Tickets are Better than One: Fair and Accurate Hiring Under Strategic LLM Manipulations
67. ICML 2025Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos
68. ICML 2025What makes an Ensemble (Un) Interpretable?
69. ICML 2025X-Hacking: The Threat of Misguided AutoML
70. ICML 2025You Get What You Give: Reciprocally Fair Federated Learning
71. NeurIPS 2025A Unified Framework for Fair Graph Generation: Theoretical Guarantees and Empirical Advances
72. NeurIPS 2025Additive Models Explained: A Computational Complexity Approach
73. NeurIPS 2025Adjacent Words, Divergent Intents: Jailbreaking Large Language Models via Task Concurrency
74. NeurIPS 2025Boosting the Uniqueness of Neural Networks Fingerprints with Informative Triggers
75. NeurIPS 2025Causally Reliable Concept Bottleneck Models
76. NeurIPS 2025Correcting misinterpretations of additive models
77. NeurIPS 2025Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing
78. NeurIPS 2025Discovering Opinion Intervals from Conflicts in Signed Graphs
79. NeurIPS 2025Discretization-free Multicalibration through Loss Minimization over Tree Ensembles
80. NeurIPS 2025Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values
81. NeurIPS 2025Does Representation Guarantee Welfare?
82. NeurIPS 2025DPA: A one-stop metric to measure bias amplification in classification datasets
83. NeurIPS 2025Efficient Fairness-Performance Pareto Front Computation
84. NeurIPS 2025Fair Continuous Resource Allocation with Equality of Impact
85. NeurIPS 2025Fair Deepfake Detectors Can Generalize
86. NeurIPS 2025Fair Matroid Selection
87. NeurIPS 2025Fair Representation Learning with Controllable High Confidence Guarantees via Adversarial Inference
88. NeurIPS 2025FairDICE: Fairness-Driven Offline Multi-Objective Reinforcement Learning
89. NeurIPS 2025Fairness under Competition
90. NeurIPS 2025Fairness-aware Anomaly Detection via Fair Projection
91. NeurIPS 2025Fairness-aware Bayes Optimal Functional Classification
92. NeurIPS 2025FairNet: Dynamic Fairness Correction without Performance Loss via Contrastive Conditional LoRA
93. NeurIPS 2025Faithful Group Shapley Value
94. NeurIPS 2025FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
95. NeurIPS 2025From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes
96. NeurIPS 2025Geometric Imbalance in Semi-Supervised Node Classification
97. NeurIPS 2025Guiding LLM Decision-Making with Fairness Reward Models
98. NeurIPS 2025Homogeneous Algorithms Can Reduce Competition in Personalized Pricing
99. NeurIPS 2025Incentivizing Time-Aware Fairness in Data Sharing
100. NeurIPS 2025Individual Fairness In Strategic Classification

FAQ

How many Social aspects · fairness papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

358 in total: 36 in 2023, 79 in 2024, 132 in 2025, 111 in 2026.

How many Social aspects · fairness papers are at NeurIPS 2026?

NeurIPS 2026 has 43 Social aspects · fairness papers, versus 62 the year before.

How many Social aspects · fairness papers are at ICLR 2026?

ICLR 2026 has 4 Social aspects · fairness papers, versus 8 the year before.

How many Social aspects · fairness papers are at ICML 2026?

ICML 2026 has 60 Social aspects · fairness papers, versus 61 the year before.

How many Social aspects · fairness papers are at CVPR 2026?

CVPR 2026 has 4 Social aspects · fairness papers, versus 1 the year before.

Which conference publishes the most Social aspects · fairness papers?

NeurIPS, with 180 papers from 2023 to 2026.