Open Access Open Access  Restricted Access Subscription Access

Artificial Intelligence at the Crossroads of Privacy and Intellectual Property: Redefining Ownership, Consent, and Control in the Data-Driven Era

Joseph Oluwaseyi Oloyede

Abstract


The rise of artificial intelligence (AI) has intensified debates at the intersection of privacy and intellectual property (IP) law, where questions of ownership, consent, and control over data have become central. AI systems rely heavily on massive datasets, often incorporating personal information and creative outputs, raising concerns about privacy rights and data protection. Simultaneously, the generation of new content by AI challenges traditional IP regimes, blurring the boundaries of authorship, inventorship, and ownership. This paper explores how the convergence of privacy and intellectual property frameworks is reshaping legal and ethical norms in the data-driven era. It examines key issues such as the legitimacy of data collection and consent, the commodification of personal information, the ownership of AI-generated works, and the control mechanisms available to individuals and organizations. Through an analysis of emerging regulations, case law, and ethical principles, the study highlights the need for integrated legal frameworks that balance innovation with individual rights. Ultimately, it argues that achieving fairness and accountability in AI requires redefining ownership, consent, and control to safeguard human dignity, privacy, and creative expression in a rapidly evolving technological landscape.


Full Text:

PDF

References


Raza, A., Yaseen, A., Khalid, S., Naqvi, S. B. R., & Noreen, U. (2023). From bytes to boundaries: Finding the fate of privacy law in the era of technology. International Journal of Contemporary Issues in Social Sciences, 2(3), 1553-1561.

Pedro M. Fernandes, AI Training and Copyright: Should Intellectual Property Law Allow Machines to Learn?, Bioethica (Oct 2024)

Lingjuan Lyu et al., Privacy and Robustness in Federated Learning: Attacks and Defenses, arXiv (2020)

arXiv

Mingfu Xue et al., Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations, arXiv (2020)

arXiv

Haonan Zhong et al., Copyright Protection and Accountability of Generative AI: Attack, Watermarking and Attribution, arXiv (2023)

arXiv

Michael Veale, Reuben Binns & Lilian Edwards, Algorithms that Remember: Model Inversion Attacks and Data Protection Law, arXiv (2018)

arXiv

Claudio Novelli et al., Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity, Computer Law & Security Review (Nov 2024)

ScienceDirect

Amy Winograd, Loose-Lipped Large Language Models Spill Your Secrets: The Privacy Implications of Large Language Models, Harvard JLTech (2023)

jipel.law.nyu.edu

Pamela Samuelson, Generative AI Meets Copyright, Science (2023) & Allocating Ownership Rights in Computer-Generated Works (1985)

Wikipedia

Good Models Borrow, Great Models Steal: Intellectual Property Rights and Generative AI, Policy and Society (2023) .Oxford Academic


Refbacks

  • There are currently no refbacks.