Licensing Artificial Intelligence: A Strategic Perspective

December 28, 2024

Abstract

In this weeks newsletter we explore the nuances of licensing AI systems, offering actionable insights for organizations navigating the complexities of AI agreements. Key topics include distinctions between data processing and training, considerations for public versus private models, and the implications of emerging practices such as fine-tuning and retrieval-augmented generation (RAG).

Introduction

Licensing AI systems has become a pivotal issue as organizations increasingly integrate these technologies into their operations. The legal and ethical challenges surrounding data usage, intellectual property, and liability are far-reaching, highlighting the need for clear and effective licensing agreements. This whitepaper outlines the critical components of AI licensing and provides recommendations for aligning legal agreements with organizational objectives.

The Importance of Licensing AI

AI systems often require significant interaction with proprietary data to function effectively, but these interactions can expose organizations to risks, such as inadvertent data disclosure or misuse. Consider the following:

  • Data Security Risks: Public AI models may incorporate input data into their training sets, potentially exposing sensitive information in future outputs.

  • Operational Impact: Poorly defined licensing terms can lead to unintended uses of data, creating legal and reputational risks.

To address these challenges, organizations must negotiate licensing terms that clarify data ownership, permissible uses, and restrictions.

Key Licensing Considerations

1. Processing vs. Training Data

The distinction between data processing and training is foundational to AI licensing:

  • Processing Data: Input data is used for immediate tasks such as generating responses or predictions. Providers require a license to process this data during operations.

  • Training Data: Input data used to teach or improve the AI model becomes embedded within the system, necessitating a perpetual, irrevocable license to transform and utilize it.

Organizations should ensure contracts clearly differentiate these uses and explicitly state whether input data will be used for training.

2. Public vs. Private Models

AI models can be categorized as public or private, with distinct licensing implications:

  • Public Models: Shared among multiple users, these models may use input data for training unless users opt out. Licensing agreements should define whether input data can be sublicensed or included in outputs shared with other users.

  • Private Models: Exclusively trained on proprietary data, these models provide greater control and security. Licensing should include ownership of model weights or parameters to safeguard exclusivity.

3. Ownership of Outputs

The ownership of AI-generated outputs is a contentious issue, particularly under U.S. copyright law, which does not consistently recognize machine-generated content as intellectual property. Most licensing agreements grant users non-exclusive rights to outputs, but organizations must understand the limitations of such rights, particularly when outputs are derived from public models.

Emerging Licensing Practices

1. Training, Re-Training, and Fine-Tuning

AI models can be:

  • Trained: Built from scratch using original data.

  • Re-Trained: Updated with additional data to improve functionality.

  • Fine-Tuned: Adjusted using custom data to specialize in specific tasks.

Licensing agreements should specify the permissible scope of these processes and address ownership of models resulting from fine-tuning.

2. Retrieval-Augmented Generation (RAG)

RAG allows AI systems to reference external data sources without incorporating them into the model. Licensing for RAG should:

  • Grant revocable permissions to use source data.

  • Prohibit the inclusion of source data in model training.

Recommendations for Crafting AI Licenses

  1. Define Data Usage Clearly: Explicitly separate processing and training data in licensing agreements.

  2. Address Ownership: Specify ownership rights for both inputs and outputs, and ensure fine-tuned models are treated as proprietary.

  3. Secure Public Model Agreements: Ensure public model licenses include clear terms for data sharing, sublicensing, and opt-out mechanisms.

  4. Prioritize Private Models for Sensitive Applications: Leverage private models for high-risk use cases to maintain data control and mitigate exposure.

  5. Include Safeguards for Re-Training and Fine-Tuning: Detail permissible re-training and fine-tuning activities and their implications for ownership and liability.

  6. Mitigate Risks in RAG Usage: Ensure licenses for RAG inputs are specific, revocable, and exclude use for training.

Conclusion

As the adoption of AI accelerates, so too does the need for robust licensing frameworks that balance innovation with risk mitigation. Organizations must proactively negotiate terms that protect their data, clarify ownership, and align with their strategic goals. By understanding the nuances of AI licensing, stakeholders can unlock the potential of these transformative technologies while safeguarding their interests.

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The Atlas AI Team