arXiv:2606.02247v1 Announce Type: cross Abstract: Shapley values are a principled attribution measure widely used in interpretable machine learning, but their exact computation scales exponentially with the number of players, motivating a wide range of approximation methods based on value function evaluations of sampled coalitions. This raises the question of whether approximation accuracy can be improved by adaptively selecting coalitions for evaluation based on previous evaluations. This is particularly relevant in settings where the value function is costly and the number of evaluations is
The paper addresses a critical challenge in explainable AI (XAI) as the complexity of AI models increases, demanding more efficient and accurate attribution methods.
Improved Shapley value estimation enables more robust and interpretable AI systems, fostering trust and facilitating wider adoption in sensitive applications.
The adaptive selection of coalitions for evaluation could significantly reduce the computational cost of achieving high accuracy in Shapley value estimation, making XAI more practical.
- · AI developers
- · Machine learning researchers
- · Industries requiring interpretable AI
- · Inefficient Shapley approximation methods
More widespread and cost-effective application of explainable AI techniques across various domains.
Increased adoption of complex AI models in regulated industries due to enhanced transparency and auditability.
Potentially democratizing access to powerful AI models by making their explanations less computationally intensive.
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Read at arXiv cs.LG