The high-density, decentralized copyright conflicts triggered by generative AI training require more than ad hoc solutions; they demand structural governance tools. This article argues that representative litigation settlement agreements offer a distinct institutional advantage. Beyond reducing the transaction costs associated with the "tragedy of the anticommons," these agreements generate market-visible evidence, specifically pricing signals and licensing practices, that validate the "potential market" under the fourth factor of fair use. This phenomenon constitutes procedural market-making. Through a comparative analysis of the U.S. Bartz class action settlement, this study reveals a dual motivation: a surface-level drive for risk aversion and remedy locking, and a deeper logic of constructing a training-licensing market. In the context of Chinese law, the feasibility of such agreements depends not on replicating foreign models, but on establishing three interpretive mechanisms: expanding the functional definition of "same category" claims; adopting a hybrid registration/confirmation system for indeterminate class membership; and converting the "consent" requirement under Article 57, Paragraph 3 of the Civil Procedure Law into a workable opt-out right subject to judicial scrutiny.

翻译：生成式人工智能训练所引发的高密度、分散性著作权冲突，不仅需要个案化解决方案，更需结构性治理工具。本文认为，代表性诉讼和解协议具有独特的制度优势。此类协议不仅能降低“反公地悲剧”相关的交易成本，更能生成具有市场可见度的证据——特别是价格信号与许可实践——从而在合理使用四要素检验框架下为“潜在市场”要件提供实证支撑。这一现象构成了程序性市场构建机制。通过对美国Bartz集体诉讼和解案的比较分析，本研究揭示了双重动因：表层表现为风险规避与救济锁定，深层逻辑则在于构建训练许可市场。在中国法律语境下，此类协议的可行性不取决于复制域外模式，而需建立三重解释机制：拓展“同类诉讼请求”的功能性界定；对不确定群体成员采用混合式登记/确认制度；将《民事诉讼法》第五十七条第三款中的“同意”要件转化为可操作的、受司法审查的退出权。