Problems and Prospects of Legal Regulation of Public Relations connected with the Use of Neural Networks
https://doi.org/10.17803/1729-5920.2024.207.2.140-151
Abstract
The level of digitalization has increased significantly in the current century, the speed of the Internet has increased by many times, and it is now possible to access it from different parts of the world. Today, artificial intelligence occupies a special place in the digital technology market, which is already an indispensable tool in many sectors of the economy of developed countries. The purpose of the study is to identify the main urgent problems of using neural networks, as well as to form proposals for their legal regulation. The study uses the formal logical method, the comparative legal method, analysis and synthesis, methods of induction, deduction, and abstraction. It has been established that artificial intelligence cannot yet distinguish a joke from a real command or user request, respectively, further development of these technologies is impossible without the implementation of an analog function of cognitive thinking. It is concluded that self-regulation can be the best way to regulate the use of neural networks, since the legal system of continental law, to which the Russian Federation belongs, is quite rigid and often may not have time to regulate the rapidly developing field of artificial intelligence. Self-regulation is able to provide an opportunity to convey proposals on the legalization of effective rules for organizing the activities of IT market participants, to create an effective mechanism for guaranteeing the quality and safety of artificial intelligence based on the joint property liability of members of self-regulating organizations. At the same time, it requires the adoption of legal norms on liability for the illegal use of neural networks, as was done in the United States and China. In the near future, deepfakes created on the basis of neural network technologies may become a threat to national security and cause harm to thousands of citizens.
About the Author
A. S. KiselevRussian Federation
Alexander S. Kiselev, Cand. Sci. (Law), Associate Professor, Department of Civil Law, Faculty of Law, Institute of Economics, Management and Law; Senior Researcher at the Center for Research and Expertise, Faculty of Law
Moscow
References
1. Andreev VK. Issues of the theory of legal regulation of entrepreneurship in the context of digitalization. Zhurnal Rossiyskogo Prava [Journal of Russian Law]. 2022;26(2):36-47. (In Russ.).
2. Arkhiptsev IN, Alexandrov AN, Maksimenko AV, Ozerov KI. Pornographic deepfake: fiction or virtual reality? Socio-political sciences. 2021;11(1):69-74. (In Russ.).
3. Burova NV. Data Scientist: A new profession or a return to the future (reflections on the example of the French education system). Data Science: Proceedings of the International Scientific and Practical Conference, St. Petersburg, February 5–7, 2020. St. Petersburg: SPbGEU Publ.; 2020. Pp. 73–73. (In Russ.).
4. Garifullin IM. The use of neural networks to identify fraudulent transactions. Innovative science. 2021;3:3032. (In Russ.).
5. Delfino RA. Pornographic deepfakes: The next tragic act of the phenomenon of «revenge porn» and the need to adopt a criminal law at the federal level. Aktualnye problemy ekonomiki i prava [Actual Problems of Economics and Law]. 2020;14(1):105-141. (In Russ.).
6. Ivanov VG, Ignatovsky YaR. Deepfakes: Prospects of application in politics and threats to personality and national security. Vestnik Rossiyskogo universiteta druzhby narodov [RUDN Journal of Law]. State and Municipal Management Series. 2020;7(4):379-386. (In Russ.).
7. Ivanchenko MA, Arkhipov PE. A man playing, a machine playing: The path to an ideal neural network and the prerequisites for the emergence of posthumanism. Ideas and ideals. 2021;13(1-1):151-165. (In Russ.).
8. Krasovskaya NR, Gulyaev AA. On the issue of controlling fakes, deepfakes, fake accounts on the Internet. Bulletin of the Udmurt University. Sociology. Political science. International relations. 1985;5(1):96-99. (In Russ.).
9. Krasovskaya NR, Gulyaev AA. Technologies of manipulation of consciousness when using deepfakes as an instrument of information warfare in the political sphere. Authorities. 2020;28(4):93-98. (In Russ.).
10. Kuznetsov DV, Koloskova NV. Is it possible to defeat artificial intelligence? Colloquium-journal. 2020;9-2(61):1819. (In Russ.).
11. Lazarev VV. Legal science in the light of the prospects of digitalization. Zhurnal Rossiyskogo Prava [Journal of Russian Law]. 2023;27(2):5-19. (In Russ.).
12. Leskova YuG. Self-regulation as an economic and legal phenomenon. Zhurnal rossiyskogo prava [Journal of Russian Law]. 2011;5:48-56. (In Russ.).
13. Surnina AO. Elements of global optimization of neural network models. Academy. 2017;3(18):32-36. (In Russ.).
14. Tarasov AM. Application of neural networks for control systems. Scientific research of young scientists: Collection of articles of the 4th International Scientific and Practical Conference: in 2 parts. Penza; 2020. Pp. 69–71. (In Russ.).
15. Tereshchenko LK. Transformation of the conceptual apparatus of information law in the context of digitalization. Zhurnal Rossiyskogo Prava [Journal of Russian Law]. 2022;26(12):98-110. (In Russ.).
16. Khilyuta VV. Artificial intelligence and criminal law: Is palingenesis acceptable in the context of digitalization? Zhurnal Rossiyskogo Prava [Journal of Russian Law]. 2023;27(9):90-103. (In Russ.).
17. Tsirin AM, Artemenko EA. Digital technologies and artificial intelligence as a means of preventing corruption in control (supervisory) activities: Domestic and foreign experience. Zhurnal rossiyskogo prava [Journal of Russian Law]. 2023;27(3):126-142. (In Russ.).
18. Chernogor NN. Artificial intelligence and its role in the transformation of modern law and order. Zhurnal rossiyskogo prava [Journal of Russian Law]. 2022;26(4):5-15. (In Russ.).
19. Harari YN. Nomo Deus: A Brief history of the Future. Moscow: Sinbad Publ.; 2019. (In Russ.).
20. Gutbrod M. Digital transformation in economy and law. Digital Law Journal. 2020;1(1):12-23.
21. Release Strategies and the Social Impacts of Language Models. OpenAIReport. November, 2019. Available from: https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf [cited 2023 August 29].
22. Shu Hu, Yuezun Li, Siwei Lyu. Exposing GAN-generated faces using inconsistent corneal specular highlights. Available from: https://arxiv.org/pdf/2009.11924.pdf [cited 2023 August 29].
23. Assael YM, Shillingford B, Whiteson Sh, Freitas № de. LipNet: End-to-End Sentence-level Lipreading Available from: https://arxiv.org/abs/1611.01599 [cited 2023 August 29].
Review
For citations:
Kiselev A.S. Problems and Prospects of Legal Regulation of Public Relations connected with the Use of Neural Networks. Lex Russica. 2024;77(2):140-151. (In Russ.) https://doi.org/10.17803/1729-5920.2024.207.2.140-151