Labeling of Results Generated Authonomously by Generative Artificial Intelligence.

The article examines the problem of labeling content created by generative artificial intelligence (AI) in order to ensure transparency and prevent possible legal and ethical risks. The author analyzes the complexity of distinguishing between human-made works and results generated using artificial intelligence technologies, and also provides examples of controversial cases, such as the Nikolai Ironov image generation project and deepfakes, for example, the use of the synthesized voice of actress Alena Andronova. The article also examines international approaches to regulating this area, including legislative initiatives of the European Union, China, the USA and Italy. Special attention is paid to the legal risks associated with the use of results created using artificial intelligence technologies, and labeling issues such as informing users about the content creation process (process-based) and preventing public deception (impact-based) to reduce the risks of intellectual property infringement by third parties. It highlights the need to introduce labeling (including metadata and watermarks) not only for images, but also for audio and video content in order to minimize the risks of misinformation and fraud. In addition, the author draws attention to the importance of developing appropriate legal regulation in the Russian Federation, suggesting that a general rule on labeling results created using artificial intelligence technologies should be established and implemented in various fields of application of AI technologies. As an example, an analogy is proposed with the YouTube video hosting approach, where the user is required to mark the fact of creating content using AI. It is proposed to assign the labeling obligation to the user or developer of the AI system in the absence of automatic labeling. The article also provides specific proposals for the development of regulations for inclusion in the draft Federal Law "On the Regulation of Artificial Intelligence Systems".

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Publication Details

Journal
Prolog
Published
2025-01-01
DOI
https://doi.org/10.21639/2313-6715.2025.2.5.57-67.
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Labeling of Results Generated Authonomously by Generative Artificial Intelligence.

M. V. Samartseva
Prolog
Data Mining Algorithms and Applications
article

Labeling of Results Generated Authonomously by Generative Artificial Intelligence.

M. V. Samartseva
article en

Abstract

The article examines the problem of labeling content created by generative artificial intelligence (AI) in order to ensure transparency and prevent possible legal and ethical risks. The author analyzes the complexity of distinguishing between human-made works and results generated using artificial intelligence technologies, and also provides examples of controversial cases, such as the Nikolai Ironov image generation project and deepfakes, for example, the use of the synthesized voice of actress Alena Andronova. The article also examines international approaches to regulating this area, including legislative initiatives of the European Union, China, the USA and Italy. Special attention is paid to the legal risks associated with the use of results created using artificial intelligence technologies, and labeling issues such as informing users about the content creation process (process-based) and preventing public deception (impact-based) to reduce the risks of intellectual property infringement by third parties. It highlights the need to introduce labeling (including metadata and watermarks) not only for images, but also for audio and video content in order to minimize the risks of misinformation and fraud. In addition, the author draws attention to the importance of developing appropriate legal regulation in the Russian Federation, suggesting that a general rule on labeling results created using artificial intelligence technologies should be established and implemented in various fields of application of AI technologies. As an example, an analogy is proposed with the YouTube video hosting approach, where the user is required to mark the fact of creating content using AI. It is proposed to assign the labeling obligation to the user or developer of the AI system in the absence of automatic labeling. The article also provides specific proposals for the development of regulations for inclusion in the draft Federal Law "On the Regulation of Artificial Intelligence Systems".

Prolog(2)
Openalex Percentile: Top 61%
Data Mining Algorithms and Applications
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Labeling of Results Generated Authonomously by Generative Artificial Intelligence. — M. V. Samartseva · Prolog (2025) | TGRS Research Map | TGRS