Generalized Priority-Aware Shapley Value

2026-05-14 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

A new method, the Generalized Priority-Aware Shapley Value (GPASV), has been introduced to address limitations in existing Shapley value extensions used for valuation in machine learning. Current methods require binary and acyclic pairwise priorities, which are often violated in real-world data like human preferences or multi-criterion comparisons. GPASV operates on arbitrary directed weighted priority graphs, where pairwise edges penalize order violations instead of strictly forbidding them. This new framework encompasses several classical models as boundary cases. The authors provide an axiomatic characterization for GPASV, detail its computational methods, and introduce a priority sweeping diagnostic. Applied to LLM ensemble valuation using the cyclic Chatbot Arena preference graph, GPASV demonstrates that varying the balance between graph priority and individual soft priority significantly alters valuation outcomes.

Key takeaway

For research scientists evaluating machine learning models or ensembles with complex, non-binary, or cyclic priority data, you should consider applying the Generalized Priority-Aware Shapley Value (GPASV). This method offers a more robust and realistic valuation by accommodating weighted priority graphs, allowing for nuanced insights into component contributions where traditional Shapley extensions fall short.

Key insights

GPASV extends Shapley value to handle complex, cyclic, and weighted priority graphs in machine learning valuation.

Principles

• Real-world priorities are often cyclic.
• Penalize order violations, don't forbid them.
• Balance graph priority with soft priority.

Method

GPASV is a random order value defined on arbitrary directed weighted priority graphs, characterized axiomatically, with associated computational methods and a priority sweeping diagnostic.

In practice

• Value LLM ensembles with cyclic preferences.
• Analyze multi-criterion comparisons.
• Evaluate aggregated human preferences.

Topics

• Shapley Value
• Generalized Priority-Aware Shapley Value
• Priority Graphs
• LLM Ensemble Valuation
• Chatbot Arena

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

Related on AIssential

• TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions — TN-SHAP-G efficiently computes Shapley values for graph-structured inputs using a graph-aligned tensor network surrogate.
• Towards Rigorous Explainability by Feature Attribution — Symbolic XAI methods offer rigorous alternatives to non-symbolic approaches for reliable feature importance attribution.
• From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment — SHAP offers more faithful and transferable explanations for rubric-based scoring than LLM-generated rationales.
• Fairness-Aware Federated Learning with Trajectory Shapley Value — TSV and FedTSV provide dynamic, fairness-aware aggregation for federated learning by evaluating client influence on model trajectory.
• The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity — Feature ranking cannot be simultaneously faithful, stable, and complete under collinearity.
• Aggregate Models, Not Explanations: Improving Feature Importance Estimation — Ensembling models directly improves feature importance estimation by reducing excess risk, especially for complex ML models.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.