Social aspects · accountability transparency and interpretability papers, 2023–2026

Key findings

• Social aspects · accountability transparency and interpretability went from 301 papers in 2023 to 1,357 in 2026, roughly 4.5x in three years.
• NeurIPS publishes the most Social aspects · accountability transparency and interpretability work: 1,275 papers from 2023 to 2026, 37% of the four-venue total.
• The fastest-growing overlap is with Social aspects · privacy: papers tagged with both rose from 0 in 2023 to 93 in 2026.
• Among the 5 topics under Alignment and Responsible AI, it ranks #2 by 2026 paper count; the fastest grower in the group, Social aspects · safety, is up 93x versus 4.5x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Social aspects · accountability transparency and interpretability papers

589 papers, page 12 of 12

551. NeurIPS 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
552. NeurIPS 2024The Representation Landscape of Few-Shot Learning and Fine-Tuning in Large Language Models
553. NeurIPS 2024The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track
554. NeurIPS 2024The Surprising Effectiveness of SP Voting with Partial Preferences
555. NeurIPS 2024The tree autoencoder model, with application to hierarchical data visualization
556. NeurIPS 2024Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
557. NeurIPS 2024Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox
558. NeurIPS 2024Towards Neuron Attributions in Multi-Modal Large Language Models
559. NeurIPS 2024Towards Scalable and Stable Parallelization of Nonlinear RNNs
560. NeurIPS 2024Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens
561. NeurIPS 2024Trading off Consistency and Dimensionality of Convex Surrogates for Multiclass Classification
562. NeurIPS 2024Training Data Attribution via Approximate Unrolling
563. NeurIPS 2024Training for Stable Explanation for Free
564. NeurIPS 2024Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear $q^\pi$-Realizability and Concentrability
565. NeurIPS 2024Transcoders find interpretable LLM feature circuits
566. NeurIPS 2024Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
567. NeurIPS 2024Transformer Doctor: Diagnosing and Treating Vision Transformers
568. NeurIPS 2024Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
569. NeurIPS 2024Transformers on Markov data: Constant depth suffices
570. NeurIPS 2024Transformers Represent Belief State Geometry in their Residual Stream
571. NeurIPS 2024Truth is Universal: Robust Detection of Lies in LLMs
572. NeurIPS 2024Understanding and Minimising Outlier Features in Transformer Training
573. NeurIPS 2024Understanding Information Storage and Transfer in Multi-Modal Large Language Models
574. NeurIPS 2024Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective
575. NeurIPS 2024Understanding Transformers via N-Gram Statistics
576. NeurIPS 2024Understanding Visual Feature Reliance through the Lens of Complexity
577. NeurIPS 2024Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback
578. NeurIPS 2024Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
579. NeurIPS 2024Using Noise to Infer Aspects of Simplicity Without Learning
580. NeurIPS 2024VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
581. NeurIPS 2024What Makes and Breaks Safety Fine-tuning? A Mechanistic Study
582. NeurIPS 2024What makes unlearning hard and what to do about it
583. NeurIPS 2024What Rotary Position Embedding Can Tell Us: Identifying Query and Key Weights Corresponding to Basic Syntactic or High-level Semantic Information
584. NeurIPS 2024When is an Embedding Model More Promising than Another?
585. NeurIPS 2024Where does In-context Learning Happen in Large Language Models?
586. NeurIPS 2024Who's asking? User personas and the mechanics of latent misalignment
587. NeurIPS 2024Why are Visually-Grounded Language Models Bad at Image Classification?
588. NeurIPS 2024Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training
589. NeurIPS 2024Zipper: Addressing Degeneracy in Algorithm-Agnostic Inference

FAQ

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

3,411 in total: 301 in 2023, 589 in 2024, 1,164 in 2025, 1,357 in 2026.

How many Social aspects · accountability transparency and interpretability papers are at NeurIPS 2026?

NeurIPS 2026 has 416 Social aspects · accountability transparency and interpretability papers, versus 522 the year before.

How many Social aspects · accountability transparency and interpretability papers are at ICLR 2026?

ICLR 2026 has 292 Social aspects · accountability transparency and interpretability papers, versus 211 the year before.

How many Social aspects · accountability transparency and interpretability papers are at ICML 2026?

ICML 2026 has 541 Social aspects · accountability transparency and interpretability papers, versus 337 the year before.

How many Social aspects · accountability transparency and interpretability papers are at CVPR 2026?

CVPR 2026 has 108 Social aspects · accountability transparency and interpretability papers, versus 94 the year before.

Which conference publishes the most Social aspects · accountability transparency and interpretability papers?

NeurIPS, with 1,275 papers from 2023 to 2026.