ABSTRACT
Recent developments in artificial intelligence (AI) have sparked debates about the legal implications of knowledge distillation (KD). While the fundamentals of what constitutes an IP infringement have long been established, the application of these principles to AI remains contentious. Knowledge distillation, which involves transferring knowledge from a larger teacher model to a smaller student model, often relies on outputs derived from potentially copyrighted datasets or proprietary architectures. This has led to questions about whether the process infringes upon copyright or falls under the protective umbrella of fair use, a doctrine that justifies the use of copyrighted material for purposes such as research, education, or innovation. Fair use has been commonly cited as a justification for using copyrighted material in training and fine-tuning machine learning models. However, its applicability to KD remains unclear, particularly when considering the transformative nature and commercial implications of the technology. This research examines the issue from both a technical and legal perspective, analyzing whether KD constitutes copyright infringement or if it can be legally defended under fair use. The findings aim to provide a nuanced understanding of the intersection between AI innovation and intellectual property law, contributing to ongoing discussions about how legal frameworks should evolve to address emerging technologies like KD.
Sirora, Leslie Wellington and Sinyoro, Sharon and Chaminuka, Rachelle Anesu and Bahige, Mutume David, Knowledge Distillation in Artificial Intelligence Models: Intellectual Property Infringement Versus Fair Use (June 20, 2025). Paper presented at the Conference Proceedings of the Intellectual Property Researchers of Europe Conference, held on June 19-20, 2025 in Geneva, Switzerland.
Leave a Reply