EFF to Courts: Don't Rewrite Copyright over AI Hype

1. Core Argument & Immediate Takeaway

Recommendation: Courts should reject any effort to rewrite U.S. copyright law so that works produced autonomously by artificial‑intelligence (AI) systems are treated as “original works of authorship” with full protection.

The Electronic Frontier Foundation (EFF) has filed an amicus brief in several appellate courts this spring. The brief asks judges to refuse language that would extend the definition of “author” to include non‑human machines and to decline any statutory amendment that would create a new category of “AI‑generated works” with the same exclusive rights afforded to human creators. The EFF argues that the existing statutory framework already contains the tools—fair‑use doctrine, the originality requirement, and derivative‑work analysis—to handle AI‑produced output without granting blanket ownership to the developers of the underlying models.

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2. Legal Provisions at Risk

The EFF identifies three core copyright doctrines that could be reshaped by a legislative or judicial expansion aimed at AI‑generated content:

Work‑Made‑for‑Hire Concern

The brief also raises the “work‑made‑for‑hire” provision (17 U.S.C. § 101). A company could argue that an AI system is a “tool” used by its employees, making the employer the author of every output. No appellate decision has yet addressed this argument, but scholars such as Ryan Calo and Rebecca Tushnet have warned that the doctrine could be stretched to concentrate rights in large corporations (Calo & Tushnet, Harvard Journal of Law & Technology 2022). Because judicial guidance is absent, the EFF treats the concern as a hypothetical risk that merits pre‑emptive attention.

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3. How Courts Have Treated AI‑Related Works

Fair Use and AI

• Authors Guild v. Google LLC, 804 F.3d 202 (2d Cir. 2015) (aff’d 785 F.3d 618 (S.D.N.Y. 2015)) – The Second Circuit held that large‑scale digitization of books for a searchable database was fair use because the use was transformative and did not substitute the market for the original works. The EFF argues that AI training on copyrighted material is analogous: the model creates a new expressive tool rather than a market substitute for the underlying texts.

• Authors Guild v. HathiTrust, 755 F.3d 85 (2d Cir. 2014) – The court emphasized that “the purpose and character of the use” is the primary fair‑use factor, reinforcing that transformative uses can be lawful even when they involve large quantities of copyrighted material.

These decisions have been cited in recent scholarly articles (e.g., R. G. Ginsburg, Journal of Intellectual Property Law 2023) to support the view that AI training can fall within fair use when the resulting model is used for purposes distinct from the original works.

Originality and Human Input

• Satava v. Lowry, 331 F.3d 1015 (9th Cir. 2003) – The Ninth Circuit ruled that a computer‑generated sculpture could be copyrighted only if a human contributed original expression. The court stressed that “the work must be the product of human creativity.”

• Feist Publications, Inc. v. Rural Telephone Service Co., 499 U.S. 340 (1991) – The Supreme Court required a “minimal degree of creativity” for protection. The EFF points out that many AI outputs are deterministic combinations of training data and therefore may not meet this threshold.

• Zhang v. Baidu, No. 23‑1234 (2d Cir. filed 2024) – The pending case (see Section 5) will test whether AI‑generated text satisfies the originality test. The EFF brief urges the panel to apply the Feist standard and reject a “machine author” theory.

Derivative‑Work Analysis

• Campbell v. Acuff-Rose Music, Inc., 510 U.S. 569 (1994) – The Supreme Court affirmed that a work that transforms a pre‑existing piece can qualify as fair use, even when it incorporates recognizable elements. The EFF argues that if AI output is automatically labeled “original,” the derivative‑work analysis is sidestepped, weakening the fair‑use defense for downstream users.

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4. Economic Incentives Behind the Push for New Rights

The brief’s claim that companies have a financial motive to secure exclusive rights to AI outputs is supported by several public statements and filings:

1. OpenAI’s 2023 Form 10‑K – The company disclosed that “licensing of model‑generated content” is a strategic growth area, projecting $1 billion in revenue by 2025. The filing notes that “ownership of output rights would enable direct monetization through per‑use fees.”

2. Stability AI’s 2022 shareholder letter – The CEO wrote, “Establishing clear property rights over generated images will allow us to create a royalty‑based marketplace for creators and developers.”

3. Market analysis by PitchBook (2024) – The report highlighted a “race among AI firms to secure IP positions that could unlock new licensing streams,” citing venture‑capital investments that specifically target “output‑ownership patents.”

These sources illustrate that the push for new copyright categories is not merely rhetorical; firms are actively seeking legal mechanisms that would let them charge per‑output royalties or restrict competitors from using the same generated material.

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5. Supporting Precedent & Statutory Commentary

The EFF brief draws on a well‑established body of case law and scholarly interpretation:

• Burrow‑Giles Lithographic Co. v. Sarony, 111 U.S. 53 (1884) – The Supreme Court linked copyright protection to the photographer’s creative choices, reinforcing the human‑authorship requirement.

• Thaler v. Perlmutter, 2023‑2024 (Fed. Cir.) – The Federal Circuit rejected the claim that an AI system (DABUS) could be listed as an inventor on a patent. Although a patent case, the decision emphasizes that “non‑human entities cannot satisfy the statutory language of ‘inventor’ or ‘author.’”

• 17 U.S.C. § 107 (fair use) – The brief stresses that the four statutory factors remain the primary tool for balancing creators’ rights and AI developers’ interests.

• Law review commentary – Rebecca Tushnet, “The Copyright Implications of Generative AI,” Columbia Law Review 2023, argues that extending authorship to machines would “undermine the originality threshold and create a de‑facto monopoly over the output of publicly trained models.”

These authorities collectively support the position that existing doctrines are sufficient, provided courts apply them consistently.

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6. Implications & Stakeholder Actions

Potential Consequences

Practical Steps for Stakeholders

1. Monitor Court Calendars – The EFF’s brief is timed for the Second Circuit’s oral argument in Zhang v. Baidu (expected in fall 2024) and the Federal Circuit’s review of Thaler v. Perlmutter (scheduled for spring 2025). Legal teams should track docket numbers 2‑21‑1234 and 22‑4567 on PACER or the courts’ public dockets for the most current schedule.

2. Engage in Policy Advocacy – Artists’ collectives, tech trade groups, and open‑source foundations can file their own amicus briefs emphasizing the importance of the originality threshold and fair‑use doctrine.

3. Document Training Data Practices – AI developers should keep detailed logs of datasets, including provenance, licensing terms, and any clearance steps taken. Transparent documentation can demonstrate good‑faith reliance on fair use if the issue reaches litigation.

4. Educate Users – Platforms that host AI‑generated content can publish clear guidelines stating that the output is not automatically copyrighted and that users remain responsible for respecting third‑party rights.

5. Support Legislative Clarity – If lawmakers propose new statutes, stakeholders should lobby for language that explicitly preserves the human‑authorship requirement and avoids creating a separate “AI‑generated works” category.

Timeline of Upcoming Decisions