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This paper introduces NObSP, a framework for decomposing neural network predictions into per-feature contribution functions and interaction residuals using oblique subspace projections, improving interpretability and reducing attribution errors compared to existing methods.
This paper proposes RelShap, a framework that incorporates relational constraints and data provenance into Shapley value computation, making explanations more faithful to the data-generating process. It is estimator-agnostic and composes with existing SHAP estimators while exploiting functional dependencies to reduce runtime.
The paper introduces a distribution-based framework to measure the stability of attribution methods (explainers) by quantifying the separability of feature rankings and identifying the maximum top-k ranking that remains reliable across stochastic runs.
Proposes a Shapley-value-based feature attribution framework for data masking that balances disclosure risk and data utility at the feature level, agnostic to specific masking methods.
FADEx introduces a novel local per-instance feature attribution method for explaining dimensionality reduction techniques, using Taylor expansion and Singular Value Decomposition to provide model-agnostic explanations and distortion analysis.
Proposes DAG-SHAP, a novel feature attribution method based on edge intervention for directed acyclic graphs, addressing limitations of existing Shapley value methods in capturing feature interactions and causal relationships.
This paper introduces the Attribution Contract, a specification for feature-attribution claims in generative language models, addressing ambiguities in what constitutes a feature and how attribution methods should be evaluated. It uses autoregressive and diffusion models as case studies to show when attribution is informative or misleading.
This paper proves that no feature ranking can be simultaneously faithful, stable, and complete under collinearity, characterizing the full attribution design space and providing a formally verified impossibility theorem in explainable AI.
A 16-year-old developer created sage-explainer, a Python package that approximates prediction sensitivity to features for black-box models like random forests and XGBoost, offering more stable results than centered finite differences.
Introduces a weight perturbation-based feature attribution method (XWP and XWPc) for fully connected neural networks, achieving competitive performance on standard baseline metrics.
Researchers introduce PIE, a CLT-native framework for efficient circuit discovery via feature attribution-based pruning, achieving ~40× compression in feature selection while maintaining behavioral fidelity on IOI and Doc-String tasks.