HOW SHOULD LICENSING AGREEMENTS ADDRESS AI TRAINING DATA AND MACHINE LEARNING MODELS?

Introduction

Artificial intelligence has effectively changed the whole world industry and trade, with machine learning systems being at the core of modern automation. To a great extent, these systems especially big language models (LLMs) and generative AI frameworks use huge datasets that are a mixture of publicly available and proprietary sources. However, the issue of their reliance on training data has become a significant point of legal debates that are related to the infringement of copyright, privacy of data, and the allocation of intellectual property. Among other things, the litigation such as Getty Images v. Stability AI, The New York Times v. OpenAI, and ANI v. OpenAI that the court fights with the unauthorized usage of the protected works for model training, is the main point of this fight. In the opinion of the U.S. Copyright Office, in a 2025 report on generative AI, it was stated that depending on the phase of the AI development pipeline the acts can be prima facie infringement.

This back-and-forth way of the issue reveals the problem of licensing agreements so much that they actually need to be there in order to properly deal with the AI industry's unique structure. Firstly, AI systems are different from normal software in a way that they are made up of several parts such as the base source code, datasets, model weights, and outputs, which are different IP doctrines governed by different laws. Additionally, AI models get better through retraining, which causes the temporal as well as the derivative aspect factors, which are not there in traditional licensing, to be present here. Therefore, this article considers how licensing agreements can handle these issues.

Analysis

Intellectual Property Ownership and Rights Allocation

At the core of the AI licensing debate is the issue of ownership. Artificial intelligence systems that learn from data are, in fact, a combination of several things: algorithms, data, architectures, and outputs, which are all four must be handled differently in legal contracts. The program which runs machine learning and the whole idea is protected by software copyright as a literary work, thereby the question of ownership is quite clear.

On the other hand, model weights that have been trained, numerical parameters that come from training, are a source of ambiguity. According to the U.S. Copyright Office, if the results are similar to the inputs that have been used for training, then model weights may involve the reproduction or derivative rights of the original works correspondingly. This means that they are not a separate technical category but in fact overlap functionally with the data they are based on and thus, are copyrighted.

Ownership of the data used for the training of AI is a matter that is even more complicated. The sources for data include licensed corporate repositories, user uploads, or web scraping, and each of them triggers different rights frameworks. Courts have been signalling that they are less and less tolerant of unlicensed materials for training and are therefore cutting down on the reliance of broad "fair use" defences. As a result, licensing agreements need to pinpoint data origin, provide ways of data verification, and look for third-party permissions before taking data into use.

Output ownership is as controversial as the rest of the issues mentioned here. Providers such as OpenAI and Anthropic give ownership of AI-produced outputs to users, but at the same time, intellectual property protection requires a significant amount of human input, hence the enforceability of such provisions is very limited. Currently, the law regards works generated by AI without human authorship as ineligible for copyright and therefore, they are effectively part of the public domain.

Training Data Usage Rights and Restrictions

Deals must show ways of gathering, handling, and using datasets. The differences of training, fine-tuning, validation, and deployment have a legal impact that is very accurate to drafting. It is typical for licenses to have a clause specifying that the data be used only for “research and non-commercial purposes.” Such a clause is proper in academic settings but not in AI systems that are commercially oriented. In the case of commercial licenses, it is necessary to indicate the permitted areas of application while at the same time disallowing the use of such cases as biometric surveillance or political profiling. The adoption of socially responsible AI licenses such as the BigScience OpenRAIL-M can be regarded as a huge step forward in terms of societal governance through the direct incorporation of these principles into the license text.

With GDPR in place, one of the principles for example data minimization and storage limitations require that personal data should not be stored forever. Licensing contracts should indicate the duration of data retention and the method of deletion or anonymization upon expiration. New ways for instance "machine unlearning" that alleviates a particular data influence from models can be helpful in meeting regulatory requirements for erasure.

Exclusivity clauses are basically the core of how market competition is shaped. Non-exclusive deals give licensors a chance to license the data to several developers, thus creating a competitive environment. On the other hand, exclusive licenses come with more paybacks but limit the licensor's ability to negotiate further. The balanced "sole license" one that is a hybrid, exclusive against third parties but also licensor-retained can be at the same level of mutuality.

Moreover, controls over sublicensing are similarly of great value. Licensees without a written permission are not allowed to distribute datasets to others unless they have express authorization. Under the EU AI Act, the transparency requirements make the arrangements in sublicensing very important because those who will be using models downstream need clear documentation about where the data came from and what rights they have.

Transparency, Audit Rights, and Regulatory Compliance

The EU AI Act requires developers of general-purpose AI models to disclose solid summaries of training data sources and categories. The European Commission's 2025 guidance on this makes it even more data modalities--text, image, audio--and the differences between licensed and publicly sourced materials have to be identified. Licensing agreements need to have disclosure provisions embedded in them, which would not only ensure compliance but also protect trade secrets. Clauses that define "confidential information" should be specific enough to maintain the secrecy of proprietary architectures and at the same time allow legal transparency.

Audit rights are the tools with which the accountability is ensured. Licensors ought to be able to retain the power to verify compliance through periodic audits or third-party oversight. Among other things, the agreements have to specify the audit frequency, the access to records that is allowed, the confidentiality provisions, and the sharing of the costs. Provisions giving the power to perform effective audits help in ensuring that the data are used in accordance with contractual and regulatory purposes.

The regulatory compliance duties have to be clearly set and confirmed between the parties. According to Article 28 of the GDPR if an AI vendor processes personal data of a client, then the vendor is a "processor", and written terms processing lawfully, maintaining confidentiality, and erasing the data securely at the end of the contract are necessary. CCPA also requires a notification to the data subjects about the collection of their personal data and gives them the right to opt-out from data "sales" - a term that may refer to AI training transfers.

Liability Allocation and Indemnification

AI-licensed agreements have to foresee the situation of liability coming from different vectors - copyright infringement, privacy breaches, algorithmic bias, or production of harmful content. Indemnification clauses are the means by which financial risk is shared between the parties and therefore, they are an essential element. Developers are offering various protections against third-party IP claims to attract adoption; however, such clauses usually have some limitations.

These provisions should be negotiated with the utmost care so as not to be overstepping. Licensees will also gain from the provision of mutual indemnity in a situation when the licensor’s negligent data procurement for the training causes infringement.

Warranty clauses serve the purpose of indemnity by giving assurance, in particular, that the training data was lawfully acquired, that the product is reasonably accurate, and that it is security compliant. Warranties should only cover a limited number of commercial scenarios since AI behaves in a stochastic manner which makes it impossible to guarantee that the output will be error-free. In the same way, limitation of liability clauses most of the time limit exposure to the amount that has been paid as a license fee or to a monetary ceiling that has been predefined.

Termination, Transition, and Maintenance

Termination clauses in AI licensing are significantly different from those in traditional software because of dataset and model dependencies. The first thing that agreements have to do is to differentiate between “termination for cause” which is a closure resulting from breach, regulatory violations, or misuse, and “termination for convenience” which is the option of withdrawing at one's own discretion with giving an adequate notice. A notice period of between 60 and 180 days is usually agreed upon in AI service contracts of enterprise scale.

Models that have been fine-tuned with the licensee-specific data make the exit strategies quite complicated. The parties are obliged to indicate whether the fine-tuned weights are the developer's or the licensees. Such decisions influence the competitive positioning and the valuation in M&A transactions or audits.

Maintenance provisions have to do with model updates and retraining. Since it is known that AI performance gets worse over time (a phenomenon called “model drift”), licensors need to specify in the contract how often, at what cost, and by what means of notification the updates will be. Licensees may also have the right to reject updates that change material features or unexpectedly alter model logic, if they are given enough notice. Continuous retraining, bias checking, and version control are some of the ways technical integrity can be maintained and post-deployment risks can be lowered.

Conclusion

The licensing of AI training data and machine learning models represents a frontier where intellectual property, data privacy, and contractual law interact dynamically. The preceding analysis underscores that effective AI licensing must move beyond conventional software templates to account for AI’s layered, evolving architecture and its reliance on diverse, legally sensitive data inputs.

First, intellectual property allocation requires express drafting separating ownership of source code, datasets, model weights, and outputs. Second, data usage provisions should balance flexibility for innovation with enforceable boundaries ensuring legal and ethical compliance. Third, transparency clauses are no longer optional but mandated by laws such as the EU AI Act; they must integrate audit and documentation obligations without compromising trade secrets. Fourth, allocating liability through calibrated indemnification and warranties enables both licensors and licensees to manage exposure relative to their roles. Fifth, compliance clauses incorporating GDPR and CCPA obligations are critical to maintaining lawful data processing and upholding user rights. Finally, thoughtful termination and maintenance terms preserve operational continuity, protecting both contractual parties beyond the model’s active lifecycle.

AI’s regulatory trajectory points toward heightened scrutiny of how models are trained and deployed. Thus, the most resilient agreements will be adaptive, anticipating future legal reforms while fostering transparency, fairness, and accountability. As courts continue to interpret the boundary between creativity and computation, and as legislators refine frameworks like the EU AI Act, robust licensing agreements will form the backbone of lawful, ethical AI innovation.