Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability | Paper Citation Record

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This paper cites Sanity checks for saliency maps.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Sanity checks for saliency maps

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability The Computational Complexity of Circuit Discovery for Inner Interpretability

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Openxai: Towards a transparent evaluation of model explanations

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Towards the unification and robustness of perturbation and gradient based explanations

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Towards better understanding of gradient-based attribution methods for Deep Neural Networks

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This paper cites Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai

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This paper cites On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Training data attribution via approximate unrolling

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability How to explain individual classification decisions

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Model interpretability through the lens of computational complexity

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Relatif: Identifying explanatory training samples via relative influence

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Local vs. Global Interpretability: A Computational Complexity Perspective

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This paper cites Understanding the role of individual units in a deep neural network.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Understanding the role of individual units in a deep neural network

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This paper cites Mechanistic Interpretability for AI Safety -- A Review.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Mechanistic Interpretability for AI Safety -- A Review

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Impossibility theorems for feature attribution

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability From shapley values to generalized additive models and back

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Towards monosemanticity: Decomposing language models with dictionary learning

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Truth is universal: Robust detection of lies in llms

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Low-Complexity Probing via Finding Subnetworks

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability On Training Data Influence of GPT Models

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Input similarity from the neural network perspective

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This paper cites Learning to explain: An information-theoretic perspective on model interpretation.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Learning to explain: An information-theoretic perspective on model interpretation

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Towards automated circuit discovery for mechanistic interpretability

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Detection of influential observation in linear regression

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Characterizations of an empirical influence function for detecting influential cases in regression

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Residuals and influence in regression

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Explaining by removing: A unified framework for model explanation

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Sparse Autoencoders Find Highly Interpretable Features in Language Models

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Real time image saliency for black box classifiers

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Editing Factual Knowledge in Language Models

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability The Shapley Taylor Interaction Index

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Visualizing higher-layer features of a deep network

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Interpretable explanations of black boxes by meaningful perturbation

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Causal abstractions of neural networks

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Dissecting Recall of Factual Associations in Auto-Regressive Language Models

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Data shapley: Equitable valuation of data for machine learning

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Neuron shapley: Discovering the responsible neurons

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Studying Large Language Model Generalization with Influence Functions

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability A Survey Of Methods For Explaining Black Box Models

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability F ast IF : Scalable influence functions for efficient model interpretation and debugging

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Training data influence analysis and estimation: A survey

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Data cleansing for models trained with sgd

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Does localization inform editing? surprising differences in causality-based localization vs

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Significance tests for neural networks

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Computationally efficient feature significance and importance for predictive models

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Datamodels: Predicting Predictions from Training Data

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Explaining explanations: Axiomatic feature interactions for deep networks

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability How can i explain this to you? an empirical study of deep neural network explanation methods

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Scalability vs

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Understanding black-box predictions via influence functions

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Concept bottleneck models

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability The disagreement problem in explainable machine learning: A practitioner's perspective

Reference 57

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Observation 5ddc95a8-aeb1-4ffb-85b2-625891c1b109 · outbound

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Beta shapley: a unified and noise-reduced data valuation framework for machine learning

Reference 58

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Optimal ablation for interpretability

Reference 59

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Measuring the effect of training data on deep learning predictions via randomized experiments

Reference 60

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability A unified approach to interpreting model predictions

Reference 61

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This paper cites The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

Reference 62

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 63

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Locating and editing factual associations in gpt

Reference 64

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Coresets for data-efficient training of machine learning models

Reference 65

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Observation b47d2276-3447-4c87-80de-7103df7efa6a · outbound

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Fast Model Editing at Scale

Reference 66

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability The quest for the right mediator: A history, survey, and theoretical grounding of causal interpretability

Reference 67

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Attribution patching: Activation patching at industrial scale

Reference 68

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Operationalizing the Blueprint for an AI Bill of Rights: Recommendations for Practitioners, Researchers, and Policy Makers

Reference 69

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability In-context Learning and Induction Heads

Reference 70

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability TRAK: Attributing Model Behavior at Scale

Reference 71

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability High dimensional model explanations: An axiomatic approach

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:08:50.858060Z

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Direct and indirect effects

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:08:50.802985Z

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Observation 97055b44-58c8-4f66-aee5-0ea43c03e749 · outbound

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 74

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Estimating training data influence by tracing gradient descent

Reference 75

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability A practical review of mechanistic interpretability for transformer-based language models

Reference 76

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Toward transparent ai: A survey on interpreting the inner structures of deep neural networks

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:08:50.736646Z

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This paper cites Why should i trust you?: Explaining the predictions of any classifier.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Why should i trust you?: Explaining the predictions of any classifier

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:08:50.694863Z

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Observation 13c7ed74-8e5c-4ce4-bdf9-470e554a2932 · outbound

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability U-net: Convolutional networks for biomedical image segmentation

Reference 79

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Mechanistic?

Reference 80

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Scaling up influence functions

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:08:50.558070Z

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Grad-CAM: Why did you say that?

Reference 82

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Decomposing and Editing Predictions by Modeling Model Computation

Reference 83

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability A value for n-person games

Reference 84

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Observation c02c07c7-e5fa-4a65-9b8c-606aafde6ef3 · outbound

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Learning important features through propagating activation differences

Reference 85

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 86

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability SmoothGrad: removing noise by adding noise

Reference 87

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Limetree: Interactively customisable explanations based on local surrogate multi-output regression trees

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:08:50.526569Z

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Observation df081e17-d84c-4049-b28d-a32d0e7aeef3 · outbound

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Striving for Simplicity: The All Convolutional Net

Reference 89

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability A Simple and Effective Pruning Approach for Large Language Models

Reference 90

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability The many shapley values for model explanation

Reference 91

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Axiomatic Attribution for Deep Networks

Reference 92

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Attribution Patching Outperforms Automated Circuit Discovery

Reference 93

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Bias and confidence in not quite large samples

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:08:50.464654Z

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Observation 86ce6516-dd97-4289-8181-b9c18dd16cc1 · outbound

This paper cites Investigating gender bias in language models using causal mediation analysis.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Investigating gender bias in language models using causal mediation analysis

Reference 95

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Observation 40c8db46-091a-44f9-a79e-514c8786c25a · outbound

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-09T22:08:49.479518Z

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Data banzhaf: A robust data valuation framework for machine learning

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:08:50.395228Z

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Observation 6699a44f-329f-4556-90f4-bbebab7ace34 · outbound

This paper cites Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-09T22:08:49.514280Z

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Observation 20bc62c3-e26f-4973-ad84-55627d568850 · outbound

This paper cites Gradient based Feature Attribution in Explainable AI: A Technical Review.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Gradient based Feature Attribution in Explainable AI: A Technical Review

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-09T22:08:49.518559Z

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Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability If you like shapley then you’ll love the core

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:08:50.353113Z

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Pith citing papers