Can You Legally Train AI Models on Copyrighted Content and Personal Data?
Artificial intelligence systems like ChatGPT, Claude, Google Gemini, and countless other machine learning models depend on massive datasets for training. These datasets often include copyrighted materials—books, articles, images, music, code—and personal information collected from users across the internet. As AI capabilities have exploded, so too have legal challenges regarding whether training AI models on this data constitutes copyright infringement, violates privacy laws, or breaches data protection regulations.
For companies developing AI technologies, these legal questions are not academic. Major AI companies face billion-dollar lawsuits from copyright holders alleging unauthorized use of their works. Privacy regulators worldwide are investigating whether AI training practices comply with data protection laws like the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Open-source projects have filed suits alleging that AI coding assistants violate software licenses by training on licensed repositories.
The stakes are enormous. If courts or regulators determine that current AI training practices violate copyright or privacy laws, the implications could fundamentally reshape the AI industry. Understanding the legal requirements for training AI models on copyrighted data and personal information is essential for any company developing or deploying AI technologies.
What Does Copyright Law Say About Training AI Models on Protected Works?
The Copyright Infringement Question
Copyright law grants creators exclusive rights to reproduce, distribute, display, perform, and create derivative works from their copyrighted content. When AI training involves copying millions of books, articles, images, or other copyrighted works into training datasets, does this constitute copyright infringement?
The answer depends largely on whether the training qualifies as “fair use” under 17 U.S.C. § 107. Fair use is a legal doctrine that permits limited use of copyrighted materials without permission for purposes like criticism, commentary, news reporting, teaching, scholarship, or research. The fair use analysis considers four factors:
**Purpose and Character of Use:** Is the use transformative? Commercial uses weigh against fair use, while nonprofit educational uses favor fair use. Transformative uses that create something new with a different purpose or meaning are more likely to be fair use.
**Nature of the Copyrighted Work:** Uses of factual works are more likely to be fair use than uses of highly creative works. Published works are treated more favorably than unpublished works.
**Amount and Substantiality:** Using larger portions of copyrighted works weighs against fair use, especially if the “heart” of the work is copied.
**Effect on the Market:** If the use harms the market for the original work or potential derivative markets, this strongly weighs against fair use.
Key Litigation Shaping AI Training Law
Several high-profile lawsuits are testing whether AI training constitutes fair use:
**Authors Guild et al. v. OpenAI:** A class-action lawsuit filed by prominent authors including John Grisham, George R.R. Martin, and others alleges that OpenAI trained ChatGPT on their copyrighted books without permission or compensation. The authors argue this constitutes massive copyright infringement. OpenAI contends that training large language models on publicly available text constitutes transformative fair use because the models learn patterns and relationships rather than memorizing and reproducing specific works.
**Getty Images v. Stability AI:** Getty Images alleges that Stability AI’s image generation model, Stable Diffusion, was trained on millions of copyrighted photographs from Getty’s collection without authorization. Getty argues that the AI system can generate images that closely resemble copyrighted photographs, potentially displacing the market for licensed images. Stability AI argues that the training process is transformative and that the system generates new, original images rather than copying Getty’s photographs.
**GitHub Copilot Class Action:** A lawsuit alleges that GitHub Copilot, an AI coding assistant developed by Microsoft and OpenAI, violates open-source software licenses by training on publicly available code repositories without proper attribution. The plaintiffs argue that Copilot can reproduce substantial portions of licensed code, including license headers, thereby violating license terms requiring attribution.
**The New York Times v. OpenAI and Microsoft:** The New York Times filed suit alleging that ChatGPT and Microsoft’s AI systems were trained on millions of Times articles without permission, enabling the AI to reproduce Times content and potentially displacing subscription revenue. The Times argues that the AI systems can effectively plagiarize its journalism, directly competing with its business model.
These cases will likely establish important precedents for AI training practices. Outcomes could range from broad fair use protection for AI training to requirements for licensing agreements with copyright holders.
Arguments Supporting Fair Use for AI Training
AI companies and fair use advocates argue that training models on copyrighted works should be considered fair use:
**Transformative Purpose:** AI models don’t store or reproduce copyrighted works verbatim. Instead, they learn statistical patterns, relationships, and representations that enable them to generate new content. This transformation—from specific copyrighted works to generalized statistical models—constitutes a fundamentally different purpose from the original works.
**Minimal Market Harm:** AI models trained on books don’t substitute for purchasing those books. Someone using ChatGPT or Claude doesn’t get the experience of reading a specific novel; they interact with a general-purpose language model. The AI output is typically different enough from any single training example that it doesn’t displace the market for that work.
**Technological Progress and Innovation:** Fair use has historically accommodated new technologies. Google’s book scanning project, search engine indexing, and thumbnail images in search results have all been deemed fair use despite involving copying copyrighted works. AI training could be viewed as a similar technological advancement that requires temporary copying for socially beneficial purposes.
**Analogy to Human Learning:** Humans learn by reading books, viewing art, and consuming media without obtaining licenses for educational use. AI training could be seen as analogous—the system learns from examples without commercially exploiting specific copyrighted works.
Arguments Against Fair Use for AI Training
Copyright holders and critics argue that AI training does not qualify for fair use protection:
**Commercial Exploitation:** Major AI companies are building billion-dollar businesses by training models on copyrighted works without compensation to creators. This commercial purpose weighs heavily against fair use.
**Wholesale Copying:** AI training often involves copying entire databases of copyrighted works—potentially millions of books, articles, images, or songs. This massive scale of copying exceeds traditional fair use boundaries.
**Market Substitution:** AI systems can generate content that competes directly with copyrighted works. If ChatGPT can write articles similar to New York Times journalism or if image generators can create photographs similar to Getty Images, this harms the markets for original works.
**Memorization and Reproduction:** While AI companies claim models don’t memorize training data, research has demonstrated that large language models can be prompted to reproduce substantial portions of training documents, including copyrighted text. This reproduction capacity undermines fair use arguments.
**Lack of Attribution:** Unlike traditional educational or scholarly uses that require attribution, AI training typically provides no credit to original creators, and AI-generated outputs don’t cite sources.
The legal resolution of these competing arguments will significantly impact the AI industry’s future practices.
What Data Privacy Laws Apply to AI Training?
GDPR Requirements for AI Systems
The European Union’s General Data Protection Regulation (GDPR) establishes comprehensive requirements for processing personal data. AI companies training models on data from EU residents must comply with several GDPR principles:
**Lawful Basis for Processing:** Organizations must have a legal basis for processing personal data, such as consent, contractual necessity, legal obligation, vital interests, public interest, or legitimate interests. AI training typically relies on legitimate interests, but this requires balancing organizational interests against individual rights and freedoms.
**Purpose Limitation:** Personal data collected for one purpose cannot be used for incompatible purposes without additional legal basis. Data collected for providing a service cannot automatically be used for training AI models without considering purpose compatibility.
**Data Minimization:** Organizations should collect and process only data that is necessary and adequate for specified purposes. Training AI on massive datasets of personal information may conflict with data minimization requirements.
**Transparency:** Individuals must be informed about how their personal data will be processed. Privacy policies must clearly disclose AI training uses.
**Individual Rights:** GDPR grants individuals rights including access, rectification, erasure (“right to be forgotten”), restriction of processing, data portability, and objection to processing. AI training practices must accommodate these rights.
**Automated Decision-Making:** Article 22 restricts automated decision-making with legal or similarly significant effects. AI systems making consequential decisions about individuals must comply with additional safeguards.
Several GDPR investigations are examining AI companies’ compliance. Italy temporarily banned ChatGPT in 2023 over GDPR concerns, though service was restored after OpenAI made compliance commitments. Other European regulators have opened similar investigations.
CCPA and U.S. Privacy Laws
The California Consumer Privacy Act (CCPA) and its successor, the California Privacy Rights Act (CPRA), establish privacy requirements for businesses processing California residents’ personal information. Similar laws have been enacted in Virginia, Colorado, Connecticut, and other states.
Key requirements include:
**Notice Requirements:** Businesses must provide clear notice about what personal information is collected, purposes for collection, and whether information is sold or shared.
**Right to Know:** Consumers can request disclosure of personal information collected, sources, purposes, and third parties with whom information is shared.
**Right to Delete:** Consumers can request deletion of their personal information, subject to exceptions.
**Right to Opt-Out:** Consumers can opt out of the sale or sharing of personal information.
**Sensitive Personal Information:** Special protections apply to sensitive categories like precise geolocation, biometric data, health information, and information about children.
AI companies must carefully consider how these requirements apply to training data. If training datasets include personal information about California residents, CCPA obligations may apply to the training process.
Biometric Privacy Laws
Several states have enacted biometric privacy laws imposing strict requirements on collecting and using biometric identifiers like facial recognition data, voiceprints, fingerprints, and retina scans. The Illinois Biometric Information Privacy Act (BIPA) is the most prominent, requiring:
**Written Notice and Consent:** Companies must obtain written consent before collecting biometric data, including disclosure of the purpose and duration of collection.
**Storage and Destruction Requirements:** Biometric data must be stored securely and destroyed when the purpose is satisfied or within specified timeframes.
**Prohibition on Profit:** Companies cannot sell or profit from biometric data.
AI companies training facial recognition, voice recognition, or other biometric AI systems must comply with these requirements. BIPA has generated significant litigation, with courts awarding substantial damages for violations.
What Best Practices Should AI Companies Follow for Legal Compliance?
Implementing Robust Data Governance
AI companies should establish comprehensive data governance frameworks addressing:
**Data Source Documentation:** Maintain detailed records of training data sources, including where data was obtained, applicable licenses or terms of service, and whether consent was obtained for AI training uses.
**Legal Review of Training Data:** Before training models on new datasets, conduct legal review to assess copyright status, privacy implications, and compliance with applicable terms of service or licenses.
**Consent Mechanisms:** Where required or advisable, implement clear consent mechanisms allowing users to opt in or opt out of having their data used for AI training.
**Data Minimization:** Collect only data necessary for training purposes. Consider whether personal identifiers can be removed or anonymized while preserving training utility.
**Transparency Disclosures:** Clearly disclose AI training practices in privacy policies, terms of service, and other public-facing documents.
Implementing Privacy-Preserving Training Techniques
Technical approaches can reduce privacy risks while maintaining model performance:
**Differential Privacy:** Add calibrated noise to training data or model outputs to prevent individual data points from being identified while maintaining statistical utility.
**Federated Learning:** Train models across decentralized datasets without centralizing raw data. Models learn from local data while sharing only model updates rather than underlying data.
**Synthetic Data Generation:** Create synthetic datasets that preserve statistical properties of real data without containing actual personal information.
**Data Anonymization:** Remove or obfuscate personally identifiable information before incorporating data into training sets, though true anonymization can be challenging.
**Secure Multi-Party Computation:** Enable collaborative training on sensitive data without any party accessing others’ raw data.
These technical measures can strengthen compliance arguments and reduce legal risks.
Establishing Content Filtering and Safety Controls
AI companies should implement safeguards to prevent models from reproducing copyrighted content or exposing sensitive information:
**Output Filtering:** Implement systems to detect and block outputs that reproduce substantial portions of copyrighted works.
**Memorization Detection:** Test models for tendency to memorize and reproduce training data, especially copyrighted works or personal information.
**Citation and Attribution:** Where AI outputs draw heavily on specific sources, consider implementing citation mechanisms to provide attribution.
**Adversarial Testing:** Conduct red-team exercises attempting to elicit copyrighted content or personal information from models to identify vulnerabilities.
Pursuing Licensing and Partnership Agreements
Rather than relying solely on fair use arguments, many AI companies are pursuing licensing agreements with content providers:
**Content Licensing Deals:** OpenAI has signed licensing agreements with publishers like Axel Springer, Associated Press, and Financial Times to train on their content with permission.
**Data Marketplace Partnerships:** Partner with data providers that have obtained appropriate rights and consents for AI training uses.
**Royalty and Revenue-Sharing Models:** Some proposals suggest compensating copyright holders whose works contribute to AI training, potentially through collective licensing systems similar to those used in music.
Licensing approaches provide clearer legal standing but add costs and complexity to AI development.
What Emerging Regulations Are Addressing AI and Data?
The EU AI Act
The European Union’s AI Act establishes a comprehensive regulatory framework for artificial intelligence systems. Key provisions include:
**Risk-Based Classification:** AI systems are classified based on risk levels, with high-risk systems subject to strict requirements including data governance, transparency, human oversight, and accuracy standards.
**Prohibited Practices:** Certain AI applications are banned, including social scoring systems and manipulative AI that exploits vulnerabilities.
**Transparency Requirements:** General-purpose AI models must disclose detailed information about training data, including copyrighted content used for training.
**Copyright and IP Obligations:** AI developers must respect intellectual property rights and provide summaries of copyrighted training data.
The AI Act represents the most comprehensive AI regulation globally and will influence international approaches.
U.S. Executive Orders and Agency Actions
The U.S. government is addressing AI through executive actions and agency initiatives:
**Executive Order on Safe, Secure, and Trustworthy AI:** President Biden’s October 2023 executive order directs federal agencies to develop AI standards, safety requirements, and privacy protections.
**FTC Investigations:** The Federal Trade Commission is investigating AI companies’ data practices, including whether training on personal data violates consumer protection laws.
**Copyright Office Studies:** The U.S. Copyright Office is conducting studies on AI and copyright issues, potentially informing future legislation or regulatory guidance.
**NIST AI Risk Management Framework:** The National Institute of Standards and Technology has developed voluntary frameworks for managing AI risks, including data governance principles.
State AI Legislation
Multiple states are proposing or enacting AI-specific legislation addressing training data, algorithmic transparency, and bias prevention. Companies should monitor state-level developments that may impose additional requirements beyond federal law.
Conclusion: Navigating the Complex Legal Landscape of AI Training Data
The legal requirements for training AI models on copyrighted content and personal data remain unsettled, with ongoing litigation, regulatory investigations, and legislative developments shaping the landscape. AI companies face substantial legal uncertainty regarding whether current training practices comply with copyright law, privacy regulations, and data protection requirements.
What is clear is that responsible AI development requires careful attention to these legal considerations. Companies should implement robust data governance, pursue licensing agreements where appropriate, adopt privacy-preserving techniques, and maintain transparency about training practices. As courts resolve copyright disputes and regulators enforce privacy laws, AI companies that have proactively addressed legal compliance will be better positioned to navigate the evolving regulatory environment.
The intersection of AI, copyright, and privacy law is one of the most consequential legal questions of our era. The outcomes will determine not only the business models of AI companies but also the balance between technological innovation and the rights of creators, the protection of personal privacy, and the development of socially beneficial AI systems.
Contact Rock LAW PLLC for AI Compliance and Data Privacy Counsel
At Rock LAW PLLC, we provide comprehensive legal guidance for companies navigating the complex requirements of AI development, data privacy compliance, and intellectual property protection. Our attorneys understand both the technological aspects of AI systems and the evolving legal frameworks governing data use, copyright, and privacy.
We offer strategic counsel on:
- Copyright compliance for AI training data
- Fair use analysis and litigation defense
- Content licensing and data acquisition agreements
- GDPR, CCPA, and other privacy law compliance
- Data governance frameworks for AI development
- Terms of service and privacy policy drafting
- Regulatory investigations and enforcement defense
- AI ethics and responsible development practices
Whether you’re developing large language models, computer vision systems, or other AI technologies, we can help you structure data practices that support innovation while managing legal risks and complying with applicable laws.
Contact us today to discuss your AI development practices and learn how we can help you navigate copyright, privacy, and data protection requirements.
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