#shapley-values News & Analysis

7 articles tagged with #shapley-values. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles

AIBullisharXiv – CS AI · 3d ago7/10

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Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values

Researchers introduce ShaQ, a Shapley-value-based framework that identifies which specific parts of user input cause uncertainty in large language models, rather than just flagging overall uncertainty. The method achieves state-of-the-art ambiguity detection on multiple benchmarks and demonstrates practical value in high-stakes domains like clinical settings by enabling targeted input clarification.

AINeutralarXiv – CS AI · Apr 147/10

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Regional Explanations: Bridging Local and Global Variable Importance

Researchers identify fundamental flaws in Local Shapley Values and LIME, two widely-used machine learning interpretation methods that fail to reliably detect locally important features. They propose R-LOCO, a new approach that bridges local and global explanations by segmenting input space into regions and applying global attribution methods within those regions for more faithful local attributions.

AINeutralarXiv – CS AI · 2d ago6/10

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Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection

Researchers compared five post-hoc explainability methods for interpreting deep learning models trained to detect Major Depressive Disorder from EEG data. While different attribution approaches showed partially overlapping patterns emphasizing frontal and temporal brain regions, the study reveals methodological assumptions significantly influence interpretability results, cautioning against treating findings as definitive clinical biomarkers.

AINeutralarXiv – CS AI · May 96/10

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Owen-Shapley Policy Optimization: A Principled RL Algorithm for Generative Search LLMs

Researchers introduce Owen-Shapley Policy Optimization (OSPO), a reinforcement learning algorithm that improves how language models learn from feedback by attributing credit to individual tokens rather than treating entire sequences as atomic units. The method addresses a fundamental training gap in generative AI systems used for recommendation tasks, showing measurable improvements on real e-commerce datasets.

AINeutralarXiv – CS AI · May 96/10

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Amortized Linear-time Exact Shapley Value for Product-Kernel Methods

Researchers introduce PKeX-Shapley, an algorithm that computes exact Shapley values for product-kernel machine learning models in quadratic time, eliminating the need for approximations. The method exploits the multiplicative structure of product kernels to achieve linear-time-per-feature attribution without sampling or density estimation, extending beyond predictive models to statistical discrepancy measures like MMD and HSIC.

AINeutralarXiv – CS AI · Apr 206/10

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Towards Rigorous Explainability by Feature Attribution

A new research paper challenges the rigor of popular explainability methods in machine learning, particularly Shapley values and SHAP, arguing that non-symbolic approaches lack the mathematical foundation needed for high-stakes applications. The work advocates for symbolic methods as a more reliable alternative for determining feature importance in AI models.

AIBullisharXiv – CS AI · Mar 24/107

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Joint Distribution-Informed Shapley Values for Sparse Counterfactual Explanations

Researchers introduce COLA, a framework that refines counterfactual explanations in AI models by using optimal transport theory and Shapley values to achieve the same prediction changes with 26-45% fewer feature modifications. The method works across different datasets and models to create more actionable and clearer AI explanations.

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