Does Using Open-Source Datasets to Train AI Violate Copyright?
1. Introduction: Open-Source Does Not Mean Copyright-Free
Modern AI models—Stable Diffusion, Midjourney, LLaMA, and others—are often trained using massive open-source datasets such as:
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LAION-5B
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COCO
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ImageNet
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OpenImages
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community-generated image sets
Many people assume that “open-source” means:
❌ free to use
❌ free from copyright
❌ automatically legal for AI training
In reality, most open-source datasets contain copyrighted works collected without permission.
As highlighted in my thesis:
“Open-source datasets have a high probability of containing copyrighted artistic works collected without authorization.”
Thus, using open-source datasets for AI training is legally risky.
2. What Exactly Is an Open-Source Dataset? (Not What People Think)
In practice, open-source datasets:
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are scraped automatically from the internet
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are created without verifying copyright status
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include images from social media, art portfolios, blogs, and online stores
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do not come with licenses for commercial AI training
Example: LAION-5B, the dataset behind Stable Diffusion.
LAION provides metadata but no license for using the images it references.
Therefore:
❌ images in LAION are not “open-source”
✔ only the metadata is open-source
This is a crucial distinction often misunderstood.
3. Does Using Open-Source Datasets Violate Copyright?
Short answer: Often YES.
Why?
Because these datasets:
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include copyrighted artworks
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were gathered without opt-in consent
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are repurposed for commercial AI training
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do not provide attribution to creators
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allow models to generate outputs resembling specific artworks
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deprive artists of economic and moral rights
Under Indonesian Copyright Law (UU 28/2014), this triggers potential violations of:
❌ Economic rights (Art. 8, 9)
❌ Moral rights (Art. 5, 7)
❌ Criminal liability for commercial use (Art. 113)
Thus, the act of using open-source datasets for training AI can be considered unlawful reproduction and unlicensed use.
4. Why Open-Source Data Mining Is Legally Problematic
Web scraping tools cannot distinguish between:
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public domain images
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licensed images
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personal photos
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copyrighted artworks
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commercial illustrations
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content prohibited for reuse
As a result, open-source datasets often contain:
✔ professional photography
✔ copyrighted illustrations
✔ digital paintings
✔ personal photos
✔ commercially licensed images
✔ creative works protected under international law
none of which were provided with consent.
5. International Perspectives on Open-Source Datasets
A. United States (Fair Use? Unclear.)
AI companies argue that training is “fair use,” but:
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courts have NOT confirmed this
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training involves copying entire works
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fair use is not guaranteed for commercial AI systems
In Getty Images v. Stability AI, the court emphasized that:
The presence of Getty-owned images in LAION does NOT make training lawful.
Open-source metadata does not immunize infringement.
B. European Union (Strict Rules)
Under the EU Copyright Directive (2019):
✔ Text and Data Mining (TDM) is allowed for research
❌ Commercial TDM requires that rightsholders did NOT opt out
Artists in Europe may explicitly block AI training.
The EU AI Act (2024) further requires:
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transparency about training data
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documentation of dataset sources
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respect for copyright rights of creators
Using open-source datasets with illegal content becomes a regulatory violation.
C. Japan (More Permissive but Not Unlimited)
Japan allows AI training more broadly, but:
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not when it harms the market
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not when it produces near-identical outputs
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developers remain responsible for misuse
Japan permits training, but infringement still applies to outputs and commercial use.
6. Are Open-Source Datasets Safe for Commercial AI?
No.
In fact, they pose one of the biggest legal risks for AI companies.
Developers may face lawsuits if:
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the dataset contains copyrighted material
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outputs resemble copyrighted works
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the dataset was collected without consent
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training is monetized
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artists suffer financial harm
Open-source does NOT eliminate liability.
7. How Can AI Developers Use Datasets Legally?
Here are the best practices followed by responsible AI companies:
✔ 1. Use datasets with clear licenses
CC0, public domain, explicit commercial licenses.
✔ 2. Build your own dataset
Using legally sourced content with contractual permission.
✔ 3. Implement licensing partnerships
Examples:
Shutterstock × OpenAI, Getty Images × Nvidia
These partnerships solve legal risks through compensation.
✔ 4. Provide dataset documentation
Transparency is required under the EU AI Act.
✔ 5. Offer creator opt-out mechanisms
Increasingly required in Europe.
8. Conclusion
Does using open-source datasets for AI violate copyright?
➡ Often, yes—especially if the dataset includes copyrighted works collected without permission.
Does open-source mean license-free?
➡ No. Open-source ≠ copyright-free.
Are AI developers liable?
➡ Yes. Training AI on unlicensed datasets exposes developers to legal sanctions.
What is the safest approach?
✔ licensed datasets
✔ creator consent
✔ compensation models
✔ transparency
Open-source datasets represent the largest legal vulnerability in today’s AI development ecosystem.
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