现有数据经济模式让数据生成者无法获利，论文提出新框架促公平分配。
A Sustainable AI Economy Needs Data Deals That Work for Generators
- 分析73份公开数据交易，发现价值多集中于数据聚合方
- 创作者实际分成接近零，且合同条款普遍不透明
- 提出EDVEX框架，推动数据价值链各方共赢
我们指出，机器学习价值链因数据处理中的经济不平等而结构性不可持续：从原始数据到模型权重再到合成输出的每个环节，技术信号不断优化，但数据生成者的经济收益却被剥离。通过对73份公开数据交易的分析，我们发现多数价值流向聚合方，创作者的实际分成接近零，且合同条款普遍存在不透明现象。这不仅是经济福利问题，更威胁当前学习算法赖以生存的反馈循环。我们识别出三大结构性缺陷——溯源缺失、议价能力不对称、定价机制静态化——是造成不平等的操作根源。本文沿着机器学习价值链条追溯这些问题，并提出均衡数据价值交换（EDVEX）框架，以建立一个至少让所有参与者受益的最小市场。最后，我们梳理了社区可贡献的研究方向，并与相关视角进行对照定位。
原文摘要 · Abstract (English)
We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality: each state in the data cycle from inputs to model weights to synthetic outputs refines technical signal but strips economic equity from data generators. We show, by analyzing seventy-three public data deals, that the majority of value accrues to aggregators, with documented creator royalties rounding to zero and widespread opacity of deal terms. This is not just an economic welfare concern: as data and its derivatives become economic assets, the feedback loop that sustains current learning algorithms is at risk. We identify three structural faults - missing provenance, asymmetric bargaining power, and non-dynamic pricing - as the operational machinery of this inequality. In our analysis, we trace these problems along the machine learning value chain and propose an Equitable Data-Value Exchange (EDVEX) Framework to enable a minimal market that benefits all participants. Finally, we outline research directions where our community can make concrete contributions to data deals and contextualize our position with related and orthogonal viewpoints.
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