arXiv:2605.20620v1 Announce Type: new Abstract: Shapley-based data valuation provides a principled way to quantify the contribution of training data, but its high computational cost makes it impractical in dynamic settings where tasks and training players evolve. Existing methods treat Shapley computation as a one-shot process and collapse contributions into aggregated scores, preventing reuse and requiring recomputation under any change. We introduce a new perspective that represents Shapley values as a player-by-task matrix and formulates dynamic valuation as a structured matrix maintenance
The increasing complexity and scale of AI models necessitate more efficient methods for data valuation and interpretability, especially in dynamic environments.
Efficient Shapley computation in dynamic settings can unlock more effective and fair data valuation, improving the performance and trustworthiness of AI systems at scale.
The ability to dynamically assess and attribute value to training data changes how AI models can be trained, updated, and governed, moving from static to adaptive data valuation.
- · AI developers and researchers
- · Data marketplaces
- · Organizations with dynamic AI workloads
- · AI ethics and auditing firms
- · Companies relying on static, one-shot data valuation methods
- · AI systems lacking transparency in data contributions
More efficient and interpretable AI training, particularly in scenarios with evolving data and tasks.
Improved resource allocation for data acquisition and labeling, leading to more cost-effective and performant AI.
Enhanced regulatory compliance and trust in AI systems due to clearer attribution of data impact on model decisions.
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Read at arXiv cs.LG