Representative Litigation Settlement Agreements in Artificial Intelligence Copyright Infringement Disputes: A Comparative Reflection Based on the U.S
Chanhou Lou

TL;DR
This paper explores how representative litigation settlement agreements can serve as effective governance tools in AI copyright disputes, fostering market signals and licensing practices to address decentralized conflicts.
Contribution
It introduces the concept of procedural market-making through settlement agreements and compares U.S. and Chinese legal contexts for their implementation and challenges.
Findings
Settlement agreements reduce transaction costs in AI copyright disputes.
They generate market-visible evidence like pricing signals and licensing practices.
Feasibility in China depends on legal interpretive mechanisms and system adaptations.
Abstract
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…
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Taxonomy
TopicsLaw, AI, and Intellectual Property · Dispute Resolution and Class Actions · Artificial Intelligence in Law
