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, recovers precedence-only and weight-only Shapley variants as special cases, and is uniquely characterized by natural axioms. We develop an efficient adjacent-swap Metropolis-Hastings sampler for scalable Monte Carlo estimation and analyze limiting regimes induced by extreme priority weights. Experiments on data valuation (MNIST/CIFAR10) and feature attribution (Census Income) demonstrate more structure-faithful allocations and a practical sensitivity analysis via our proposed "priority sweeping".

翻译：沙普利值广泛用于模型无关的数据估值和特征归因，但其隐含假设贡献者可互换。当贡献者存在依赖关系（如复用/增强数据或因果特征排序）或需根据信任、风险等因素调整贡献时，这一假设可能产生问题。我们提出优先级感知沙普利值（PASV），该框架同时融合硬性优先约束和贡献者特定的软性优先级权重。PASV适用于一般优先序结构，可分别恢复纯优先序和纯权重沙普利变体作为特例，并通过自然公理进行唯一刻画。我们开发了高效的邻域交换Metropolis-Hastings采样器以支持可扩展蒙特卡洛估计，并分析了由极端优先级权重诱发的极限机制。在数据估值（MNIST/CIFAR10）和特征归因（Census Income）上的实验表明，该方法能实现更符合结构的分配，并通过我们提出的"优先级扫描"方法提供实用的敏感性分析。