Overview
The training of AI models has created a new category of data licensing with distinct considerations from traditional data use agreements. When data is used to train a machine learning model, the data's influence persists in the model's parameters even after the original data is no longer directly accessible. This raises questions that traditional copyright and data licensing frameworks were not designed to address: Is training a "copy"? Does the licensor retain any rights in outputs generated by models trained on their data? What happens to trained models when a data license expires?
Software licensing for AI training has become a contentious area. Traditional software licenses contemplate human use of software outputs. The use of software, databases, or online content as training data for AI systems sits awkwardly within existing license grants. Whether existing licenses permit such use, and what additional permissions are required, has become a significant commercial and legal question for AI developers and content owners alike.
Intellectual property in AI-generated work presents perhaps the most fundamental challenge. Indian copyright law requires human authorship—a work created entirely by AI may not be protectable. But most AI outputs involve significant human input at various stages: in training data curation, in prompt engineering, in selection and editing of outputs. Determining who owns what in this human-AI collaboration requires careful contractual allocation.
Key Considerations
Training Use Rights
Explicitly addressing whether data may be used for machine learning training, the scope of permitted training, and any restrictions on the types of models that may be trained.
Model Residual Rights
Defining what rights (if any) the data licensor retains in models trained on their data, and what obligations apply to such models after license expiration.
Output Ownership Allocation
Establishing clear ownership and usage rights for AI-generated outputs, addressing the human-AI authorship questions that current law leaves unclear.
Synthetic Data Rights
Addressing whether and how synthetic data generated from licensed data may be created, used, and shared, including derivative work considerations.
Attribution and Provenance
Establishing whether and how the origin of training data must be disclosed or attributed in AI systems or their outputs.
Compliance with Data Rights
Ensuring that AI training complies with underlying data rights, including personal data protections, database rights, and copyright in compiled datasets.
Applying the TCL Framework
Technical
- Understanding the training process and how data influences model behavior
- Assessing data provenance and underlying rights in training datasets
- Evaluating whether models can be "unlearned" if required to address data rights issues
- Understanding the relationship between training data and model outputs
- Reviewing data quality, bias, and representativeness requirements
Commercial
- Pricing models for training data—per-use, per-model, revenue share
- Valuing the contribution of training data to AI system commercial value
- Allocating risk of IP challenges to training data use
- Structuring ongoing royalties or use fees for trained models
- Addressing competitive restrictions on training data use
Legal
- Drafting explicit training use grants within license scope
- Addressing the copyright status of AI outputs under Indian law
- Structuring ownership allocation for human-AI collaborative works
- Including representations about training data provenance and rights
- Addressing moral rights and attribution in AI contexts
"The most valuable asset in AI is often the training data. Yet most data agreements predate AI and don't contemplate training use. The gap between what existing licenses permit and what AI development requires creates both risk and opportunity—risk for those who assume permissions that don't exist, opportunity for those who structure new agreements that capture this value."
Common Pitfalls
Assumed Training Rights
Assuming that standard data or software licenses permit AI training use when many do not contemplate this use case.
Ignoring Model Persistence
Treating data licenses as expiring cleanly without addressing the persistence of data influence in trained models.
Unclear Output Rights
Failing to address ownership of AI-generated outputs, leaving critical IP questions to uncertain legal doctrines.
Data Provenance Gaps
Not verifying the underlying rights in training data, creating liability exposure when data has been improperly sourced.
Oversimplified Ownership
Assigning all AI output IP to one party without considering the various contributions and their legal implications.
IP and Data Framework
Indian copyright law requires human authorship—works created without human creative input may not be protectable. The Copyright Act's provisions on computer-generated works address works created "by or under the circumstances" of computers, but AI-generated content tests these boundaries. Database rights in India are less developed than in the EU, affecting protection for compiled datasets. DPDPA imposes restrictions on using personal data for AI training—consent requirements, purpose limitations, and data subject rights apply. The intersection of copyright, database rights, contract, and data protection law creates a complex framework requiring careful contractual navigation.
Practical Guidance
- Explicitly address AI training in all data and content licenses—both as licensor and licensee.
- Document training data provenance and maintain audit trails of data rights.
- Include clear ownership allocation for AI outputs, specifying the basis of each party's rights.
- Address model residual issues—what happens to trained models when data licenses end?
- Consider whether exclusive training rights or competitive restrictions are appropriate.
- Include representations about compliance with underlying data rights, including personal data.
Frequently Asked Questions
Related Practice Areas
Need Assistance with AI Data Licensing?
Our team brings deep expertise in ai and technology matters.