Data access models for training systems that support natural language dialogue (AI chatbots based on LLM) as a key element in the development of artificial intelligence in Russia and abroad: legal aspects

Artificial intelligence (AI) technologies, systems, and algorithms are rapidly developing in Russia as well as abroad. The issue of data for training systems that support natural language dialogue (LLM-based chatbots) is becoming increasingly important from the perspective of access to them and compared to the approaches of countries like the USA and China and their unions like the European Union that are developing artificial intelligence parallel to Russia in the direction of obtaining "strong" AI. This study also helps better understand Russia's position in ratings assessing the level of development of artificial intelligence relations. When considering this issue, the author assumes an objective-subjective approach to the world and assumes the emergence of AI as a part of objective reality and a natural process of human development, relying on dialectics. The scientific novelty and theoretical and practical significance are determined by the purpose of the research, the range of sources, and the problems studied. Among the conclusions reached by the author, for example, are the following: under conditions where data is becoming (or has already become) a new oil, it is necessary 1—to overcome the bottlenecks of the new economic system—the data economy - and the onset of "digital feudalism"—a situation in which basic resources and access to them are controlled by a limited number of digital platforms (private or public), which do not provide access to businesses, 2—to spread regional initiatives (for example, the Experimental Legal Regime (ELR) of Moscow, or the Data Lakes of industrial enterprises in the Republic of Tatarstan, Russian Federation, or the ELR in Shanghai) to other entities and/or regions. Because regional "Lakes" are useless without federal integration, 3—provide businesses with the free access to anonymized data from housing, transportation, public procurement, and other areas. This can be achieved by creating a mechanism to monetize data for businesses (for example, a national data market similar to Microsoft Azure, where businesses can exchange data for tax benefits, could be created).