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 3,411 篇，第 57 / 69 页

2801. ICML 2024Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis
2802. ICML 2024Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
2803. ICML 2024Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation
2804. ICML 2024Position: Measure Dataset Diversity, Don't Just Claim It
2805. ICML 2024Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized
2806. ICML 2024Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback
2807. ICML 2024Position: Social Environment Design Should be Further Developed for AI-based Policy-Making
2808. ICML 2024Position: Stop Making Unscientific AGI Performance Claims
2809. ICML 2024Positive and Unlabeled Learning with Controlled Probability Boundary Fence
2810. ICML 2024Pricing with Contextual Elasticity and Heteroscedastic Valuation
2811. ICML 2024Probabilistic Constrained Reinforcement Learning with Formal Interpretability
2812. ICML 2024Promoting External and Internal Equities Under Ex-Ante/Ex-Post Metrics in Online Resource Allocation
2813. ICML 2024Prospector Heads: Generalized Feature Attribution for Large Models & Data
2814. ICML 2024Prototypical Transformer As Unified Motion Learners
2815. ICML 2024Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
2816. ICML 2024Representation Surgery: Theory and Practice of Affine Steering
2817. ICML 2024Representing Molecules as Random Walks Over Interpretable Grammars
2818. ICML 2024Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective
2819. ICML 2024Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
2820. ICML 2024Risk Estimation in a Markov Cost Process: Lower and Upper Bounds
2821. ICML 2024Robust and Conjugate Gaussian Process Regression
2822. ICML 2024Scaling Down Deep Learning with MNIST-1D
2823. ICML 2024Scaling Exponents Across Parameterizations and Optimizers
2824. ICML 2024Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
2825. ICML 2024Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
2826. ICML 2024SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization
2827. ICML 2024SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity
2828. ICML 2024Sparse and Structured Hopfield Networks
2829. ICML 2024Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models
2830. ICML 2024Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
2831. ICML 2024Stability and Multigroup Fairness in Ranking with Uncertain Predictions
2832. ICML 2024Stacking Deep Set Networks and Pooling by Quantiles
2833. ICML 2024Symmetry Induces Structure and Constraint of Learning
2834. ICML 2024The Emergence of Reproducibility and Consistency in Diffusion Models
2835. ICML 2024The Entropy Enigma: Success and Failure of Entropy Minimization
2836. ICML 2024The Good, The Bad, and Why: Unveiling Emotions in Generative AI
2837. ICML 2024The Linear Representation Hypothesis and the Geometry of Large Language Models
2838. ICML 2024Tilt and Average : Geometric Adjustment of the Last Layer for Recalibration
2839. ICML 2024Topological Neural Networks go Persistent, Equivariant, and Continuous
2840. ICML 2024Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components
2841. ICML 2024Towards Compositionality in Concept Learning
2842. ICML 2024Towards efficient deep spiking neural networks construction with spiking activity based pruning
2843. ICML 2024Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context
2844. ICML 2024Transformers, parallel computation, and logarithmic depth
2845. ICML 2024Transforming and Combining Rewards for Aligning Large Language Models
2846. ICML 2024Trustless Audits without Revealing Data or Models
2847. ICML 2024Understanding and Diagnosing Deep Reinforcement Learning
2848. ICML 2024Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
2849. ICML 2024Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
2850. ICML 2024Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs

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。