arXiv:2602.01399v2 Announce Type: replace Abstract: The Shapley value is a ubiquitous framework for attribution in machine learning, encompassing feature importance, data valuation, and causal inference. However, its exact computation is generally intractable, necessitating efficient approximation methods. While the most effective and popular estimators leverage the paired sampling heuristic to reduce estimation error, the theoretical mechanism driving this improvement has remained opaque. In this work, we provide an elegant and fundamental justification for paired sampling: we prove that the
The increasing complexity and scale of machine learning models necessitate more efficient and theoretically sound methods for interpretability, particularly for critical applications.
Improved understanding and approximation of Shapley values are crucial for reliable AI systems, enabling better feature importance, data valuation, and causal inference for strategic decision-making.
The theoretical justification for paired sampling in Shapley value estimation provides a stronger foundation for developing more accurate and computationally feasible AI interpretability tools.
- · AI researchers
- · Machine learning engineers
- · Companies using interpretable AI
- · Regulatory bodies in AI
- · Black-box AI models in critical applications (eventually)
More robust and explainable AI models become feasible across various industries.
Increased trust and adoption of AI in high-stakes environments due to clearer attribution and accountability.
New AI-driven products and services emerge that rely heavily on transparent and provable decision-making processes.
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Read at arXiv cs.LG