AI Algorithms and Trade Secrets: a Legal Exploration of Intellectual Property Rights
Abstract
The rapid advancement of artificial intelligence has drawn significant attention to the protection of AI algorithms through intellectual property rights (IPR). Of the various forms of IPR, trade secrets have emerged as a key means of protecting proprietary artificial intelligence technologies. This study examines the legal framework for protecting artificial intelligence algorithms as trade secrets, exploring the associated complexities and challenges. Employing a qualitative research design, the paper conducts a comparative legal analysis of case studies and content analysis of relevant legal documents. Key issues identified by the researcher include the tension between trade secret protection and the need for transparency in artificial intelligence, the challenges of enforcing protection due to the technical complexity of its algorithms, and the potential ethical conflicts that arise from prioritising secrecy over public accountability. Additionally, author of the study compares trade secret protection with other forms of IPR, such as patents and copyrights, to evaluate their effectiveness in the artificial intelligence domain. The findings suggest that, while trade secrets offer significant advantages in protecting artificial intelligence algorithms, they also present challenges in ensuring transparency, ethical artificial intelligence development, and innovation. The study concludes with policy recommendations aimed at improving the legal frameworks for trade secret protection while balancing the need for public interest and innovation. The research contributes to the ongoing discourse at the intersection of artificial intelligence, law, and ethics, providing valuable insights for policymakers, legal professionals, and artificial intelligence developers.
References
Aplin T. (2015) Right to property and trade secrets. In: C. Geiger (ed.) Research handbook on human rights and intellectual property. Cheltenham: Edward Elgar Publishing, 727 p. DOI: https://doi.org/10.4337/9781783472420.00035
Barfield W., Pagallo U. (2020) Advanced introduction to law and artificial intelligence. Cheltenham: Edward Elgar Publishing, 208 p. DOI: https://doi.org/10.4337/9781789905137
Brant J., Lohse S. (2014) Trade secrets: Tools for innovation and collaboration. SSRN Electronic Journal. DOI: https://doi.org/10.2139/ssrn.2501262
Crittenden W.F., Crittenden V.L., Pierpont, A. (2015) Trade secrets: Managerial guidance for competitive advantage. Business Horizons, vol. 58, no. 6, pp. 607–613. DOI: https://doi.org/10.1016/j.bushor.2015.06.004
D’Souza C. (2019) Big data and trade secrets (a general analysis). SSRN Electronic Journal. DOI: https://doi.org/10.2139/ssrn.3316328
Fan Y., Hao G., Wu J. (2022) Transferable unique copyright across AI model trading: A blockchain-driven non-fungible token approach. In: IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C). New York: IEEE Press, pp. 102–105. DOI: https://doi.org/10.1109/QRS-C57518.2022.00023
Franzoni L.A., Kaushik A.K. (2016) The optimal scope of trade secrets law. International Review of Law and Economics, no. 45, pp. 45–53. DOI: https://doi.org/10.1016/j.irle.2015.11.004
Girasa R. (2020) Intellectual property rights and AI. In: Artificial intelligence as a disruptive technology. Cham: Springer International Publishing, pp. 217–254. DOI: https://doi.org/10.1007/978-3-030-35975-1_7
Gulyamov S.S. (2023) AI authorship and ownership of intellectual property in industrial power and control systems. In: 5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). New York: IEEE Press, pp. 217–221. DOI: https://doi.org/10.1109/SUMMA60232.2023.10349471
Kardos V. (2022) Data protection challenges in the era of artificial intelligence. Central and Eastern European eDem and eGov Days, no. 341, pp. 285–294. DOI: https://doi.org/10.24989/ocg.v341.21
Katyal S. (2019) The paradox of source code secrecy. Cornell Law Review, vol. 104, pp. 1183–1279. Available at: https://scholarship.law.cornell.edu/clr/vol104/iss5/2
Koshiyama A. et al. (2021) Towards algorithm auditing: A survey on managing legal, ethical and technological risks of AI, ML and associated algorithms. SSRN Electronic Journal. DOI: https://doi.org/10.2139/ssrn.3778998
Maggiolino M. (2019) EU trade secrets law and algorithmic transparency. SSRN Electronic Journal. DOI: https://doi.org/10.2139/ssrn.3363178
Malgieri G. (2016) Trade secrets v personal data: A possible solution for balancing rights. International Data Privacy Law, vol. 6, no. 2, pp. 102–116. DOI: https://doi.org/10.1093/idpl/ipv030
Marsch N. (2019) Artificial intelligence and the fundamental right to data protection: Opening the door for technological innovation and innovative protection. In: Regulating artificial intelligence. Cham: Springer International Publishing, pp. 33–52. DOI: https://doi.org/10.1007/978-3-030-32361-5_2
Matulionyte R., Hanif A. (2021) A call for more explainable AI in law enforcement. In: IEEE 25th International Enterprise Distributed Object Computing Workshop. New York: IEEE Press, pp. 75–80. DOI: https://doi.org/10.1109/EDOCW52865.2021.00035
Oliinyk O. (2023) Creative industries in the epoch of artificial intelligence: Tendencies and challenges. In: Culture and Contemporaneity. Almanac. DOI: https://doi.org/10.32461/2226-0285.2.2023.293736
Pedraza-Farina L.G. (2017) Spill your (trade) secrets: Knowledge networks as innovation drivers. SSRN Electronic Journal. DOI: https://doi.org/10.2139/ssrn.2944701
Pereira Dias Nunes D. (2015) The European Trade Secrets Directive (ETSD): Nothing new under the sun? SSRN Electronic Journal. DOI: https://doi.org/10.2139/ssrn.2635897
Pu K. (2023) Intellectual property protection for AI algorithms. Frontiers in Computing and Intelligent Systems, vol. 2, no. 3, pp. 44–47. DOI: https://doi.org/10.54097/fcis.v2i3.5210
Qiu Y.-H. et al. (2021) Investigating the impacts of artificial intelligence technology on technological innovation from a patent perspective. Applied Mathematics and Nonlinear Sciences, vol. 6, no. 1, pp. 129–140. DOI: https://doi.org/10.2478/amns.2021.1.00015
Rizvi A.T. et al. (2021) Artificial intelligence (AI) and its applications in Indian manufacturing: A review. In: Lecture notes in mechanical engineering. Singapore: Springer Singapore, pp. 825–835. DOI: https://doi.org/10.1007/978-981-33-4795-3_76
Soni P. (2023) A study on artificial intelligence in finance sector. International Journal of Creative Research Thoughts, vol. 9, no. 5, pp. 223–232. Available at: https://www.irjmets.com/uploadedfiles/paper//issue_9_september_2023/44676/final/fin_irjmets1695121219.pdf
Tan Z.Q., Wong H.S., Chan C.S. (2022) An embarrassingly simple approach for intellectual property rights protection on recurrent neural networks, arXiv. DOI: https://doi.org/10.18653/v1/2022.aacl-main.8
Taneja A. et al. (2023) Artificial intelligence: Implications for the agri-food sector. Agronomy, vol. 13, no. 5, p. 1397. DOI: https://doi.org/10.3390/agronomy13051397
Ten Teije A. et al. (eds.) (2017) Artificial intelligence in medicine: proceedings of the 16th conference on artificial intelligence in medicine. Vienna, June 21–24, 2017. Cham: Springer International Publishing (Lecture Notes in Computer Science). DOI: https://doi.org/10.1007/978-3-319-59758-4
Xu H. (2024) Case study of a class-action lawsuit against Tesla’s monopolistic practices based on its price discrimination strategy. Finance & Economics, vol. 1, no. 7, pp. 1–4. DOI: https://doi.org/10.61173/hc0aky39
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