arXiv:2604.08438v2 Announce Type: replace Abstract: The Shapley value, and its broader family of semi-values, has received much attention in various attribution problems. A fundamental and long-standing challenge is their efficient approximation, since exact computation generally requires an exponential number of utility queries in the number of players $n$. To meet the challenges of large-scale applications, we explore the limits of efficiently approximating semi-values under a $\Theta(n)$ space constraint. Building upon a vector concentration inequality, we establish a theoretical framework
The increasing complexity and scale of AI models necessitate more efficient methods for understanding and attributing feature importance, driving research into approximation techniques like Adalina.
Efficiently approximating Shapley values is crucial for explainable AI in large-scale applications, enabling better model interpretability and reliability for strategic decision-making.
The ability to approximate semi-values with a $\Theta(n)$ space constraint makes explainable AI techniques viable for larger and more complex systems without prohibitive computational cost.
- · AI developers
- · Explainable AI (XAI) researchers
- · Industries using large-scale AI models
- · Traditional, computationally intensive XAI methods
- · Organizations unable to adopt efficient attribution techniques
Improved interpretability and trustworthiness of advanced AI models.
Faster deployment of complex AI systems in high-stakes environments due to clearer understanding of their decision-making processes.
Enhanced regulatory acceptance and adoption of AI in sectors requiring transparency and accountability.
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