arXiv:2602.09326v2 Announce Type: replace Abstract: Shapley values are widely used for model-agnostic data valuation and feature attribution, yet they implicitly assume contributors are interchangeable. This can be problematic when contributors are dependent (e.g., reused/augmented data or causal feature orderings) or when contributions should be adjusted by factors such as trust or risk. We propose Priority-Aware Shapley Value (PASV), which incorporates both hard precedence constraints and soft, contributor-specific priority weights. PASV is applicable to general precedence structures, recove
The increasing complexity and opacity of AI models necessitates more nuanced methods for understanding contributions, especially with the rise of agentic systems and complex data pipelines.
This development improves auditability, fairness, and interpretability in advanced AI systems, which is critical for deployment in sensitive applications and for addressing regulatory concerns.
The ability to attribute value more precisely, considering dependencies and priorities, alters how AI models are built, evaluated, and potentially regulated, allowing for more robust and accountable AI.
- · AI ethicists and researchers
- · Developers of complex AI models
- · Industries requiring high AI interpretability (e.g., finance, healthcare)
- · Data scientists
- · Developers relying on opaque 'black box' AI
- · Systems with poorly documented data dependencies
Improved methods for understanding AI contributions will enhance model debugging and optimization.
This could lead to new standards and best practices for AI development that emphasize auditable and interpretable components.
More transparent AI could accelerate adoption in highly regulated sectors, potentially de-risking advanced AI deployments.
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Read at arXiv cs.LG