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Doe v GitHub Inc

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Case Information
Case name Doe v. GitHub, Inc.[1]
Court U.S. District Court, Northern District of California; U.S. Court of Appeals for the Ninth Circuit.[1]
Docket 4:22-cv-06823-JST (district court); 24-7700 (appeal).[1]
Filed November 3, 2022 (original complaint).[2]
Judge(s) Jon S. Tigar (district court); Sidney R. Thomas, Eric D. Miller and Stanley Blumenfeld Jr. (September 16, 2026 appellate panel; Miller wrote the opinion).[1]
Plaintiffs J. Doe 1 and J. Does 2–5, programmers proceeding individually and on behalf of a proposed class.[1]
Defendants GitHub, Inc.; Microsoft Corporation; OpenAI, Inc. and the affiliated OpenAI entities identified in the appellate caption.[1]
Case type Proposed class action concerning AI-generated code, copyright-management information and open-source licenses.[1]
Claims / issues DMCA § 1202(b) and breach of contract; output attribution, training-stage theory preservation and Article III standing.[1]
Status September 16, 2026: dismissal of the DMCA claim affirmed on interlocutory appeal; two contract claims remain pending in district court.[1]

Doe v. GitHub, Inc. is a proposed class action by programmers against GitHub, Microsoft and OpenAI entities concerning Copilot and Codex and attribution of open-source code. On September 16, 2026, the Ninth Circuit affirmed dismissal of the DMCA claim; two contract claims remained pending.[1]

Background

The original complaint was filed on November 3, 2022. It alleged that the defendants’ use of licensed material violated attribution requirements and asserted DMCA, contract and other claims. These are allegations, not findings of liability.[2] After amendments and dismissals, the appellate opinion described the remaining claims as one DMCA claim and two contract claims.[1]

September 16, 2026 appellate decision

The appeal concerned an interlocutory order dismissing the section 1202(b) claim. The Ninth Circuit found plaintiffs had adequately alleged a substantial risk of injury for standing purposes, but held that the pleaded output theory failed to allege removal or alteration of copyright-management information from an existing copy. The complaint instead described a statistical generation process producing new works that never contained that information.[1]

The panel declined to consider a training-stage removal theory because plaintiffs had forfeited it below. The court also explained that works need not be literally identical to support an inference that copyright-management information was removed; minor cosmetic changes do not necessarily defeat liability. It expressly took no position on whether output similarities could support ordinary copyright infringement. The decision did not resolve the two contract claims still pending before the district court.[1]

See September 16, 2026 digest coverage.

References

  1. ↑ 1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.11 1.12 Ninth Circuit, Doe v. GitHub, Inc., No. 24-7700, opinion filed September 16, 2026, pp. 1–2, 7–18.
  2. ↑ 2.0 2.1 Doe v. GitHub, Inc., No. 3:22-cv-06823, original complaint, ECF 1, filed November 3, 2022, PDF pp. 1–4; copy hosted by plaintiffs’ counsel.
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