Regulatory Approaches to AI-Generated Video Content

Yatama Zahra; Amirah

doi:10.70356/josapen.v4i2.115

Authors

Yatama Zahra Faculty of law, Universitas Sriwijaya
Amirah Department of Publisher, Lentera Ilmu Publisher

DOI:

https://doi.org/10.70356/josapen.v4i2.115

Keywords:

Artificial Intelligence, Video Content, Regulatory Approaches

Abstract

The rapid advancement of artificial intelligence (AI) has transformed the creation and distribution of video content, enabling the production of highly realistic synthetic media at unprecedented speed and scale. While AI-generated videos offer significant benefits in education, entertainment, marketing, and communication, they also create complex legal and regulatory challenges involving misinformation, privacy violations, intellectual property infringement, impersonation, and public trust. This study examines existing regulatory approaches to AI-generated video content and assesses their effectiveness in addressing emerging legal and governance challenges. Using a qualitative literature review and comparative policy analysis, the research examines regulatory frameworks across jurisdictions, focusing on transparency requirements, content labeling, accountability mechanisms, content moderation, privacy protection, and liability provisions. The findings indicate that existing regulations often struggle to keep pace with technological developments, creating gaps in enforcement, transparency, and legal certainty. The study proposes a balanced regulatory framework that promotes innovation while strengthening accountability, transparency, and protection of individual rights.

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References

OECD. (2019). Recommendation of the Council on Artificial Intelligence. OECD.

UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. UNESCO.

European Union. (2016). Regulation (EU) 2016/679 (General Data Protection Regulation).

Burrell, J. (2016). How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512

NIST. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.600-1

Republic of Indonesia. (2022). Law No. 27 of 2022 concerning Personal Data Protection.

Badan Pemeriksa Keuangan Republik Indonesia. (2022). Undang-Undang Nomor 27 Tahun 2022 tentang Pelindungan Data Pribadi.

Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–705. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2765268

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. https://doi.org/10.1145/3287560.3287598

Mirsky, Y., & Lee, W. (2021). The creation and detection of deepfakes: A survey. ACM Computing Surveys, 54(1), Article 7. https://doi.org/10.1145/3425780

U.S. Copyright Office. (2024). Copyright and Artificial Intelligence, Part 1: Digital Replicas. U.S. Copyright Office.

Citron, D. K., & Chesney, R. (2019). Deepfakes and the new disinformation war. Foreign Affairs, 98(1), 147–155.

Brundage, M., Avin, S., Clark, J., et al. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. Future of Humanity Institute, University of Oxford. https://doi.org/10.17863/CAM.22520

European Union. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act).

Kietzmann, J., Lee, L. W., McCarthy, I. P., & Kietzmann, T. C. (2020). Deepfakes: Trick or treat? Business Horizons, 63(2), 135–146. https://doi.org/10.1016/j.bushor.2019.11.006