The launch of Meta’s Llama 2 has undoubtedly ignited a surge of enthusiasm for open-source large language models (LLMs), marking a significant milestone as the first commercially licensed open-source LLM from a major tech player. However, amidst this excitement lies a daunting specter: the legal complexities surrounding intellectual property and copyright issues, especially concerning derivative works generated by AI.

Understanding the Poison Pill of Derivatives

Within the current ecosystem of generative AI, particularly with tools like Llama 2, the implications of derivative works may represent a critical “poison pill.” As organizations rush to leverage these technologies, there exists an underlying assumption that the burden of regulatory compliance falls solely on the developers of LLMs. This assumption, while convenient, is not just naive; it overlooks the intricate web of legal challenges that derivative works may conjure.

Derivative works, in copyright terms, traditionally refer to modifications or adaptations of pre-existing copyrighted content. However, the rise of open-source LLMs complicates this landscape. Questions arise: If an AI model generates output based on copyrighted material, how do we classify that output legally? Is it entirely derivative, or is it something else entirely? As this ambiguity pervades the generative AI field, the potential for disputes looms large.

The Perfect Storm: Why LLMs Change the Game

The intersection of legal uncertainty and AI advancement creates a perfect storm characterized by three main factors:

• Copyright Claims: If the courts determine that training models on copyrighted materials constitutes infringement, the repercussions could be severe, impacting enterprises that utilize such models.
• Output Implications: The distinction between the model and its outputs becomes significant. While vendors may argue that the core models are not infringing, the generated content might still be subject to copyright claims.
• User Responsibility: With potential liabilities arising from output, companies and individuals leveraging these AI systems must grapple with the associated risks of using them in commercial environments.

Risk Management Strategies for Enterprises

For enterprises, navigating this complex landscape requires a multi-faceted approach to risk management. While traditional software development faced analogous challenges with viral and copyleft licenses, generative AI introduces an entirely new set of considerations. Observations can be drawn from the open-source movement, which once staggered between permissive licenses (like Apache 2.0) and more restrictive ones (such as AGPL), and parallels can be identified for LLM vendors.

Here are some strategies enterprises might adopt:

• Clarifying Input Data: Ensure that models are trained using clearly defined input data with well-established usage rights. This can help mitigate risks of copyright claims stemming from training datasets.
• Traceability: Emphasize traceable usage rights, allowing enterprise leaders to track the sources of their training data and the outputs generated.
• Collaborative Approaches: Differentiate among LLM vendors who seek to push risk onto users and those willing to collaborate with their customers in risk management efforts.

Looking Ahead: The Road to Navigating AI’s Legal Terrain

As we transition from the uncharted territory that defined the early days of AI development, the importance of balancing LLM capabilities with comprehensive risk management cannot be overstated. While the allure of open-source LLMs offers pathways to enhance commercial strategies, practitioners must remain acutely aware of the looming copyright risks that these models encapsulate.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations. To mitigate the emerging risks associated with generative AI, businesses and legal stakeholders alike must collaborate and innovate, paving the way toward a secure and thriving landscape.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.