Rethinking Data Value: Asymmetric Data Shapley for Structure-Aware Valuation in AI Model Markets
Xi Zheng, Yinghui Huang, Xiangyu Chang, Ruoxi Jia, Yong Tan
Modern AI model markets assemble models from heterogeneous pipeline contributions, including raw data, derivative data, and data-related services, whose value can depend on upstream inputs and inherited model states. Conventional valuation approaches focus on data contributions without explicitly accounting for these production relationships. They can therefore misattribute value by evaluating contributions outside their upstream production context. We formalize this pipeline-context valuation mismatch and develop Asymmetric Data Shapley (ADS), a unified framework for structure-aware value attribution. ADS translates documented production relationships into ordered valuation groups and evaluates contributions among peers conditional on their upstream production context. We provide an axiomatic characterization of the resulting allocation and quantify allocation uncertainty when evidence supports multiple valuation structures. To support valuation at larger scales or with costly utility evaluations, we develop a Monte Carlo estimator (MC-ADS) and a surrogate-assisted estimator (SA-MC-ADS). Experiments show that ignoring production dependencies systematically shifts value toward redundant derivative data and away from useful upstream data and services. Moreover, experiments on derivative data selection and on dataset procurement during multi-stage large language model fine-tuning show that decisions guided by ADS improve downstream model performance relative to the respective benchmarks.
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