Decades in Business, Technology and Digital Law

Negotiating Data License for AI Training: Key Considerations

by | May 5, 2024 | Firm News

The data used to train artificial intelligence (AI) models plays a crucial role in determining the effectiveness, efficiency, and fairness of these systems. As businesses and researchers seek to acquire data through licensing, understanding the intricacies of data licensing agreements becomes paramount.

This blog post explores the essential issues to consider when negotiating a license to use data for training AI models.

  1. Data Scope and Usage Rights

The first consideration in a data licensing agreement is defining the scope of the data and the rights for its use. Licensees need to ensure that the data provided is sufficient in both quantity and quality to train effective models. This involves:

    • Volume and Variety: Ensure the dataset is large and diverse enough to train robust models.
    • Specific Use: Clarify whether the data can be used for commercial purposes, research, or both.
    • Exclusivity: Determine if the rights are exclusive or non-exclusive. Exclusive rights can be more costly but may provide competitive advantages.
  1. Compliance with Data Privacy Laws

With the implementation of regulations such as the GDPR in Europe and CCPA in California, compliance with data privacy laws is more critical than ever. Licensees must understand:

    • Anonymization and Pseudonymization: Ensure that personal data is adequately anonymized or pseudonymized to comply with privacy laws.
    • Data Subject’s Rights: Consider the rights of individuals whose data is being used, including their rights to access, correct, and delete their data.
    • Cross-Border Data Transfers: Be aware of restrictions on data transfer across geographic boundaries, which might affect how data can be stored and accessed.
  1. Data Accuracy and Quality

The quality of the data directly impacts the performance of the AI models trained. Licensees should consider:

    • Accuracy and Reliability: Verify the accuracy, timeliness, and reliability of the data provided.
    • Bias and Fairness: Assess the data for biases that could lead to unfair or unethical AI outcomes. Implement strategies to mitigate these biases.
  1. Intellectual Property Rights

Understanding and negotiating the intellectual property rights associated with the data is crucial. This includes:

    • Ownership of Derived Data: Clarify who owns the data generated from using the licensed data, such as new models or enhanced datasets.
    • Attribution Requirements: Check if the data provider requires attribution and how it should be given.
  1. Cost and Payment Terms

The financial aspects of a data licensing agreement can significantly affect the overall value of a deal. Considerations include:

    • Pricing Models: Understand the pricing model—whether it is based on volume, duration of use, or specific data segments.
    • Renewal and Exit Terms: Be clear about the terms for renewing the license and the conditions under which the agreement can be terminated.
  1. Liability and Indemnification

Liability clauses define the responsibility if something goes wrong with the data or its use:

    • Warranties: Ensure that there are warranties confirming the data’s compliance with laws and that it is as described.
    • Indemnification: Negotiate indemnification clauses to protect against potential legal issues arising from data use.
  1. Support and Maintenance

Ongoing support and maintenance are essential for long-term success:

    • Updates and Corrections: Ensure there are provisions for updating the data and correcting any inaccuracies.
    • Technical Support: Consider whether technical support is offered and the level of service provided.


Negotiating a data license for AI training involves a thorough understanding of numerous factors, from the legalities of data usage and privacy to the practicalities of data quality and cost. By carefully considering these issues, organizations can secure the data they need to build powerful and responsible AI systems, while ensuring compliance and maximizing the value of their investments.