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AII-1001-11357
Well documentedThree or more independent publishers report the core facts.Supporting sources are pending verification.

Microsoft Blog Post Promoted Training AI on Pirated Harry Potter Books

Microsoft published a developer blog tutorial in late 2024 that instructed users to download pirated copies of the Harry Potter book series to train AI models on Azure. The post, written by a senior product manager, has since been removed, but was cached and shared widely before deletion.

20 February 2026 Β· Microsoft Β· Intellectual property and appropriation
20 February 2026
AII-1001-11357
Quick facts
Category
Intellectual property and appropriation
Country
United States
Date
20 February 2026
Drafted by
Raidu Incident Detection

What happened

Microsoft published a developer blog post in late 2024 written by Pooja Kamath, a Microsoft Senior Product Manager, that directed users to obtain pirated copies of the Harry Potter novels through a Kaggle dataset to train Azure-based AI models. The blog post, which was a technical tutorial on adding generative AI to applications via Azure, suggested using the series to create a Q&A system or auto-generate Harry Potter fan fiction. The post was accompanied by an LLM-generated image of two children on a train with a Microsoft logo between them.

Reported impact

The Kaggle dataset, mistakenly marked as 'public domain,' was downloaded approximately 10,000 times before the blog post and dataset were removed from the site. Microsoft did not initially offer a public explanation for the removal, though the incident reignited discussion about the tech industry's relationship with copyright law in generative AI development.

Governance controls

The blog post and associated Kaggle dataset were subsequently removed from Microsoft's website, though both remained accessible via the Internet Archive.

Classification

Category
Intellectual property and appropriation
Industry
Media, social platforms and marketing
Country
United States
Harm status
Actual harm
Intent
Unintentional
Timing
Post-deployment
Harm types
Economic or property
Harmed
Business
Failure modes
Training data misuse, Unsafe recommendation
Consequences
Content removal
AI systems
Azure

Sources and evidence

Sources not yet published

This record was researched and written by the Index from original reporting. Its sources are still being verified by an editor, and they are listed here once that check is complete. Until then the record carries no reviewer.

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