社会层面 · accountability transparency and interpretability论文趋势 2023–2026

主要结论

• 社会层面 · accountability transparency and interpretability论文从 2023 年的 301 篇增长到 2026 年的 1,357 篇，三年间扩大到约 4.5 倍。
• NeurIPS 是发社会层面 · accountability transparency and interpretability论文最多的会议，2023–2026 年共 1,275 篇，占四个会议总量的 37%。
• 交叉方向里涨得最快的是社会层面 · privacy：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 93 篇。
• 在「对齐与负责任 AI」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的社会层面 · safety是 93 倍，社会层面 · accountability transparency and interpretability是 4.5 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026A Unifying Perspective on Language Model Interpretability
• ICML 2026$\texttt{PRISM}$:A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
• ICLR 2026Automated Interpretability Metrics Do Not Distinguish Trained and Random Transformers
• CVPR 2026Beyond Matching to Tiles Bridging Unaligned Aerial and Satellite Views
• NeurIPS 2025$\mathcal{X}^2$-DFD: A framework for e$\mathcal{X}$plainable and e$\mathcal{X}$tendable Deepfake Detection
• ICML 2025(How) Can Transformers Predict Pseudo-Random Numbers?
• ICLR 2025Adaptive Transformer Programs: Bridging the Gap Between Performance and Interpretability in Transformers
• CVPR 2025Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability
• NeurIPS 20242D-OOB: Attributing Data Contribution Through Joint Valuation Framework
• ICML 2024Attention Meets Post-hoc Interpretability: A Mathematical Perspective
• ICLR 2024"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection
• CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
• NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
• ICML 2023A Modern Look at the Relationship between Sharpness and Generalization
• ICLR 2023Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization
• CVPR 2023Analyzing and Diagnosing Pose Estimation With Attributions
• NeurIPS 2026AdKnob: Ad Intensity Control and Labeling for LLM-Native Advertising
• ICML 2026$\texttt{ShaplEIG}$: Bayesian Experimental Design for Shapley Value Estimation
• ICLR 2026Bandits with Single-Peaked Preferences and Limited Resources
• CVPR 2026IEBGLAn Interpretability-Enhanced Brain Graph Learning Framework with LLM-Instructed Topology and

全部社会层面 · accountability transparency and interpretability论文

共 152 篇，第 3 / 4 页

101. ICML 2024Position: Measure Dataset Diversity, Don't Just Claim It
102. ICML 2024Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized
103. ICML 2024Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback
104. ICML 2024Position: Social Environment Design Should be Further Developed for AI-based Policy-Making
105. ICML 2024Position: Stop Making Unscientific AGI Performance Claims
106. ICML 2024Positive and Unlabeled Learning with Controlled Probability Boundary Fence
107. ICML 2024Pricing with Contextual Elasticity and Heteroscedastic Valuation
108. ICML 2024Probabilistic Constrained Reinforcement Learning with Formal Interpretability
109. ICML 2024Promoting External and Internal Equities Under Ex-Ante/Ex-Post Metrics in Online Resource Allocation
110. ICML 2024Prospector Heads: Generalized Feature Attribution for Large Models & Data
111. ICML 2024Prototypical Transformer As Unified Motion Learners
112. ICML 2024Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
113. ICML 2024Representation Surgery: Theory and Practice of Affine Steering
114. ICML 2024Representing Molecules as Random Walks Over Interpretable Grammars
115. ICML 2024Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective
116. ICML 2024Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
117. ICML 2024Risk Estimation in a Markov Cost Process: Lower and Upper Bounds
118. ICML 2024Robust and Conjugate Gaussian Process Regression
119. ICML 2024Scaling Down Deep Learning with MNIST-1D
120. ICML 2024Scaling Exponents Across Parameterizations and Optimizers
121. ICML 2024Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
122. ICML 2024Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
123. ICML 2024SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization
124. ICML 2024SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity
125. ICML 2024Sparse and Structured Hopfield Networks
126. ICML 2024Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models
127. ICML 2024Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
128. ICML 2024Stability and Multigroup Fairness in Ranking with Uncertain Predictions
129. ICML 2024Stacking Deep Set Networks and Pooling by Quantiles
130. ICML 2024Symmetry Induces Structure and Constraint of Learning
131. ICML 2024The Emergence of Reproducibility and Consistency in Diffusion Models
132. ICML 2024The Entropy Enigma: Success and Failure of Entropy Minimization
133. ICML 2024The Good, The Bad, and Why: Unveiling Emotions in Generative AI
134. ICML 2024The Linear Representation Hypothesis and the Geometry of Large Language Models
135. ICML 2024Tilt and Average : Geometric Adjustment of the Last Layer for Recalibration
136. ICML 2024Topological Neural Networks go Persistent, Equivariant, and Continuous
137. ICML 2024Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components
138. ICML 2024Towards Compositionality in Concept Learning
139. ICML 2024Towards efficient deep spiking neural networks construction with spiking activity based pruning
140. ICML 2024Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context
141. ICML 2024Transformers, parallel computation, and logarithmic depth
142. ICML 2024Transforming and Combining Rewards for Aligning Large Language Models
143. ICML 2024Trustless Audits without Revealing Data or Models
144. ICML 2024Understanding and Diagnosing Deep Reinforcement Learning
145. ICML 2024Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
146. ICML 2024Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
147. ICML 2024Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
148. ICML 2024Verification of Machine Unlearning is Fragile
149. ICML 2024Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformer
150. ICML 2024Weighted distance nearest neighbor condensing

常见问题

2023–2026 年四大会议一共有多少篇社会层面 · accountability transparency and interpretability论文？

共 3,411 篇，按年份分别是 2023 年 301 篇、2024 年 589 篇、2025 年 1,164 篇、2026 年 1,357 篇。

NeurIPS 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

NeurIPS 2026 有 416 篇社会层面 · accountability transparency and interpretability论文，上一届是 522 篇。

ICLR 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

ICLR 2026 有 292 篇社会层面 · accountability transparency and interpretability论文，上一届是 211 篇。

ICML 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

ICML 2026 有 541 篇社会层面 · accountability transparency and interpretability论文，上一届是 337 篇。

CVPR 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

CVPR 2026 有 108 篇社会层面 · accountability transparency and interpretability论文，上一届是 94 篇。

哪个会议的社会层面 · accountability transparency and interpretability论文最多？

NeurIPS，2023–2026 年共 1,275 篇。