Artificial Intelligence in Russian media: opportunities, risks and prospects

Today we are talking about how artificial intelligence and neural network technologies are changing the work of the media. These changes concern not only Western media — they are actively affecting Russian journalism as well. Neural networks are already penetrating all stages of content creation — from collecting information to publishing. Their capabilities open up new horizons for journalists, but also raise important ethical, technological and political questions.

Where and how AI is used in Russian media

In Russia, as in the rest of the world, the introduction of AI into the media space is happening at a rapid pace:

  • Automation of routine tasks. Journalists are increasingly using neural networks to transcribe interviews, translate audio into text, compile summaries, and automatically correct materials. This saves time and allows them to focus on deeper analytics.

Example: The URA.RU news agency is actively implementing neural networks (ChatGPT, Yandex GPT) for rewriting news feeds, creating headlines, transcribing interviews and automatically correcting materials. This allows journalists to prepare publications faster and save editorial resources.

  • Big data analysis. Russian editorial offices use AI to work with large volumes of information, especially when preparing investigations or monitoring public opinion. For example, social media analysis helps quickly identify alarming topics and respond to them.

Example: Large editorial offices such as RIA Novosti, RBC, Interfax use AI to monitor public opinion, analyze social networks and identify trends, which speeds up the preparation of analytical and investigative materials.

  • News personalization. Media platforms use machine learning algorithms to offer users personalized content based on their interests and behavior.

Example: Zen News and Mail.ru News use machine learning algorithms to create individual news feeds, taking into account the interests and behavior of each user.

  • Monitoring social networks and trends. Using specialized tools, editorial staff monitor current topics and quickly respond to information events.

Example: big data monitoring and analytics services «SNIPR», BrandAnalytics, Medialogia provide media analytics to various resources.

Content Generation: Opportunities and Challenges

Generative neural networks are capable of creating texts, images, audio and video that are virtually indistinguishable from human ones. Russian media are already using this to write sports reviews, weather forecasts, financial news and even advertising materials.

Example: On the 360 TV channel, the weather forecast is already being voiced live by artificial intelligence, which has become a new format for presenting information to viewers.

However, there are serious risks involved:

  • Fake news and deepfakes. Artificial intelligence can be used not only to produce content, but also to use it manipulatively. In the RuNet, there are regular cases of the dissemination of fake news and deepfakes (including those involving government officials) aimed at shaping a certain public opinion.

— According to the Global Fact-Checking Network, in the first three months of 2025, the number of political deepfakes in Russia exceeded 65% of the total for the whole of 2024.
— In 2025, 89% of all recorded deepfakes concerned politics, the activities of law enforcement agencies, and national security issues. Most often, fakes were created with the participation of images of Russian governors.

  • Framing and bias. As research, including that of the AIRI Institute for Artificial Intelligence, has shown, the vast majority of models are trained on English-language data. This creates the risk of biased coverage, especially in geopolitical conflicts.
  • Labeling of AI content. To maintain audience trust, some Russian publications are starting to introduce mandatory labeling of materials created or modified using AI.

AI as a journalist’s assistant

By integrating AI into the editorial process, journalists gain a powerful tool that enhances their professional capabilities:

  • Next-generation fact-checking. In the context of information warfare and disinformation, AI is becoming an indispensable assistant in fact-checking. It allows you to find sources, compare versions of events, and quickly identify lies.

Example: AI systems are used in some news agency editorial offices to find primary sources, compare versions of events, identify false information, and automate fact-checking. Telegram has also begun testing a new feature aimed at combating disinformation. We are talking about the built-in Fact Check panel, which is displayed under publications deemed questionable.

  • Hybrid newsrooms. An effective model of the future is a collaboration between humans and machines. Algorithms help find suspicious materials, and journalists add meaning, context, and an authorial approach to them.

Example: at URA.RU, journalists and neural networks work together: algorithms help with information processing and content generation, and journalists add context, analysis, and authorial style.


Challenges and ethical dilemmas

The Russian media sphere faces a number of unique challenges associated with the implementation of AI:

  • Deepfakes control: according to experts, the number of fake videos and audios on the Internet is growing. Particularly dangerous are those that can be used in politics or to discredit government officials. Monitoring centers and labeling standards are required.
  • The need for regulation: proposals are being discussed to introduce mandatory labeling of AI content, create a regulatory framework for working with neural networks, and develop ethical principles for the use of artificial intelligence in the media.
  • Education and training: journalists need to learn how to work with new technologies, understand their capabilities and limitations. This is the only way to remain a professional in the era of digital transformation. Currently, the market is experiencing an acute shortage of specialists who can work with AI and machine learning. Leading universities (e.g., Moscow State University, Higher School of Economics) and large companies (Yandex, VK, Sber) are investing in educational programs and internships for journalists and IT specialists.

Prospects for development

According to experts, a significant transformation of the media industry will occur in the next 2-3 years:

  • Increased investment in AI: large media holdings such as Gazprom-Media, National Media Group, VK are actively investing in the development of their own AI solutions.
  • Creating a “human” brand: In a situation where the main flow of information will be produced by AI, the value of original, authorial material will increase. Perhaps in the future, such articles will be marked as “written by a human”.
  • Symbiosis of man and machine: already now, a number of editorial offices (URA.RU, RIA Novosti, RBC) are implementing models where AI takes on routine tasks, and journalists focus on analytics, investigations and creative projects.

Neural networks are not just a technological trend, but a powerful tool that can radically change journalism for the better. But at the same time, it requires a conscious approach, strict regulation and constant control.

Our task is not to be afraid of technology, but to learn to use it wisely. This is the only way we will preserve the value of reliable information, honest reporting and a free press in the era of artificial intelligence.