Navigating Data Ownership in the AI Age, Part 2: Frameworks Governing Data Ownership

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The proliferation of AI-derived and processed data in the era of big data is occurring against a complex backdrop of legal frameworks governing ownership of and responsibilities with regard to that data. In a previous installment of this two-part series, the authors outlined challenges and opportunities presented by big data and AI-derived data. In this part, they will discuss the complex legal backdrop governing this emerging area, including potential implications for business.

Patent Law and Machine-Generated Data Ownership

While not explicitly excluding machines as potential inventors, United States patent law has traditionally operated within an anthropocentric framework. This human-centric approach to inventorship and ownership is deeply ingrained in statutory law and judicial interpretations. However, rapid advancements in AI and ML technologies are increasingly blurring the lines between human and machine capabilities in the realm of invention. This creates an environment of legal uncertainty, necessitating vigilance among stakeholders in technology and IP law for future legislative or judicial developments that may clarify or redefine inventorship in the context of machine-generated innovations.

Trade Secret Protection for Machine-Generated Works

Trade secret law provides a compelling avenue for protecting machine-generated works, largely because it does not require the identification of a human inventor. This legal protection is anchored on three foundational pillars. First, the information must not be publicly disclosed or easily ascertainable, preserving its secretive status. Second, the information must possess intrinsic economic value attributable to its confidential nature. Third, reasonable measures must be undertaken to maintain the confidentiality of the information, ensuring its continued protection under trade secret law.

Given these criteria, trade secret law provides a flexible yet robust framework for safeguarding machine-generated works, circumventing the complexities and limitations often associated with copyright and patent law. This adaptability makes trade secret law increasingly relevant in the era of AI and ML, where traditional IP boundaries are being continually redefined.

The Fair Use Doctrine

The fair use doctrine stands as a nuanced yet indispensable exception within copyright law allowing for creating and utilizing transformative derivative works without constituting copyright infringement. Its relevance is heightened today, where technological advancements in big data, ML, and digital technology fundamentally alter how we interact with information.

The legal significance of the fair use doctrine has been underscored by several landmark cases illustrating its evolving role in mediating technological innovation and IP rights. For instance, the U.S. Supreme Court’s ruling in Google v. Oracle emphasized the transformative nature of Google’s use of Java APIs in the Android operating system, constituting fair use. Similarly, the Authors Guild v. Google case highlighted the public benefit of scanning and indexing millions of books, which the court deemed qualifying for fair use.

While applying this doctrine to ML-created derived data, courts may consider several factors, such as the purpose and character of the use, the nature of the copyrighted work, the amount and substantiality of the portion used, and the effect on the market value of the original work. If the ML model transforms the data in a way that could be considered “transformative use,” it might be more likely to be deemed fair use. However, ethical considerations also come into play, particularly when ML-derived data is used in ways that could be considered harmful or discriminatory.

Courts employ a multi-faceted approach in evaluating fair use claims. They scrutinize the intent behind the derivative work (‘Purpose and Character of Use’), assess the original work’s nature (factual or creative), examine the extent of the borrowed material (‘Amount and Substantiality of the Portion Used’), and evaluate the potential market impact on the original work.

It’s crucial to recognize that the fair use doctrine is not a static legal principle but a flexible and adaptable framework. It evolves in response to changing social, technological, and cultural contexts. Influenced by the norms and values of different communities and innovations in various fields, the doctrine remains relevant and applicable in a world where the modes of creation and dissemination are in constant flux.

Ethical and Societal Considerations

Beyond the legal frameworks, ethical stewardship plays a crucial role in responsible data management. Transparency, consent, and robust security measures constitute the cornerstone of responsible data management. Additionally, ethical guidelines should govern the use of data to prevent harmful or unethical applications, such as discrimination or exploitation. Public interest considerations and effective dispute resolution mechanisms should also be integrated into any comprehensive data governance framework.


In the rapidly evolving landscape of big data, AI, and the IoT, the issue of data ownership has become increasingly complex and multi-dimensional. This complexity is further accentuated by the intersection of various legal frameworks, including IP laws, trade secrets, and data protection regulations. As we have explored, each framework offers opportunities and challenges, necessitating a nuanced approach to data governance.

The advent of AI and ML technologies has introduced additional layers of intricacy, particularly in IP rights. As machines become increasingly capable of innovation, the anthropocentric frameworks of existing patent laws are being called into question, highlighting the need for legal evolution. Finally, the issue of data ownership is not solely a legal construct but also an ethical and societal one. The need for a multi-faceted approach to data governance is evident, balancing the rights and responsibilities of all stakeholders involved — individuals, machines, or public entities. Such an approach would incorporate elements of transparency, consent, security, compliance, and ethical considerations, thereby creating a governance framework that is both robust and adaptable.