AI-Generated Content and the Future of Class Action Lawsuits
The rise of generative AI is creating a legal quagmire, particularly concerning copyright and intellectual property. A recent note argues that existing legal frameworks struggle to address the unique challenges posed by AI-generated content, potentially rendering many copyright-based class action lawsuits uncertifiable. The core issue? Proving individual harm and establishing commonality in a landscape where AI models operate as “black boxes.”
The Predominance Hurdle: Amchem and Wal-Mart
Successfully launching a class action requires demonstrating “predominance”—that common questions of law or fact outweigh individual ones. Landmark cases like Amchem Products Inc. V. Windsor and Wal-Mart v. Dukes established a high bar for this requirement. The difficulty lies in tracing the use of copyrighted material within the vast datasets used to train AI models and then linking that use to concrete harm experienced by individual creators.
Understanding Generative AI: LLMs and Diffusion Models
Generative AI relies on complex neural networks. Large Language Models (LLMs) predict and generate text based on patterns learned from massive text datasets. Diffusion models, create images, audio, and other media by progressively refining randomly generated noise. The opaque nature of these models—how they learn and what data influences their outputs—complicates the process of establishing a direct link between copyrighted work and AI-generated content.
The Ascertainability Problem: Who is in the Class?
Beyond predominance, class actions require “ascertainability”—a clear way to identify class members. Determining which creators’ works were used in AI training, and whether that use caused demonstrable harm, presents a significant challenge. This represents further complicated by differing interpretations of ascertainability requirements across various circuit courts.
Statutory Damages: A False Promise?
Whereas statutory damages under the Copyright Act (17 U.S.C. § 504) might seem like a workaround, the requirement for pre-registration of copyrighted works creates an inequitable barrier. Many potential class members—particularly high-volume or resource-constrained creators—may not have registered their work, excluding them from seeking redress.
Navigating the Legal Maze: Potential Solutions
Issue Subclass Certification: A Targeted Approach
One promising strategy is to utilize Rule 23(c)(4)(A) issue subclass certification. This allows courts to isolate and resolve core liability questions—such as whether an AI model was trained on illegally obtained data—before addressing individual damages. This approach can streamline litigation and reduce the risk of unmanageable complexity.
The Role of Special Masters
Given the technical complexities of AI, appointing Rule 53 special masters—neutral experts—can be invaluable. These masters can provide specialized knowledge, manage discovery, and aid courts navigate the intricacies of AI training methodologies. This can ensure a more informed and efficient certification inquiry.
Did you know?
The Amchem case, while dealing with asbestos exposure, established a precedent for predominance that is now being applied to the novel challenges of AI-generated content litigation.
The Path Forward
The legal landscape surrounding AI and copyright is rapidly evolving. Courts are grappling with how to apply existing procedural rules to this new technology. A proactive approach—leveraging tools like issue subclass certification and special masters—can help ensure that legitimate claims are addressed while deterring frivolous lawsuits. Durable substantive rules from Congress will be needed to provide clarity and certainty in this emerging area.
Pro Tip
Focusing on common liability questions, rather than individual damages, is a key strategy for navigating the predominance hurdle in AI copyright class actions.
FAQ
Q: What is “predominance” in the context of class actions?
A: Predominance means that common questions of law or fact must outweigh individual ones, making a class action a more efficient way to resolve disputes.
Q: What are LLMs and diffusion models?
A: LLMs generate text, while diffusion models create images and other media. Both are types of generative AI powered by neural networks.
Q: Can statistical sampling be used to prove damages in these cases?
A: The use of statistical sampling is increasingly restricted, particularly for proving actual damages, due to rulings like Wal-Mart v. Dukes.
Q: What is Rule 23(c)(4)(A)?
A: This rule allows courts to certify a class for specific issues, such as liability, before addressing broader questions like damages.
Q: What is a Rule 53 special master?
A: A neutral expert appointed by the court to assist with complex technical issues in a case.
Want to learn more about the legal challenges of AI? Explore our other articles on intellectual property and technology law.
