Artificial intelligence: a mythologem of Russian scientific content

Abstract

Introduction. Artificial intelligence and neural network technologies can rightfully be considered key areas within the NBIC convergence. The history of AI research spans over fifty years. During this period, there has been a significant increase in publications not only in technical sciences but also in the social sciences and humanities. This body of research can be analyzed on various grounds. In this article, we attempt to implement one of the many options. The focus is on the concept of “transparency,” which is gaining popularity in the scientific community. The purpose of the article is to analyze the Russian Science Citation Index (RSCI) database and conceptualize the mythologem of scientific discourse in the social sciences in the field of artificial intelligence. The latter, in relation to scientific norms and ideals (objectivity, evidence, referentiality, consistency, rigor, etc.), does not correspond to scientific rigor, but is immanent to the discourses of the social sciences. The reference database of scientific articles includes works published over the course of a decade (2015–2025). The choice was driven by the trend toward the relevance and novelty of scientific data. Search and query terms included “social sciences,” “artificial intelligence,” and “transparency.” Other data (author specializations, scientific community affiliations, etc.) were not taken into account. Methods. The methodological framework is formed by the detection of verbal markers, content analysis, and representation in a tag cloud format. As in the past, emphasis was placed primarily on qualitative analysis. The selection of markers was based on their frequency of use in abstracts, titles, and keywords. Bibliographic descriptions were retained to assess the peak time point of the trend. The intermediate analytical result is expressed as a tag cloud grapheme, allowing for the assessment of word frequency and a clear representation of the semantic connections between the “transparency” metaphor and other concepts. Results. Unlike Western scholarly works, the field of “technological mythology” in Russia does not yet have clear subject and methodological boundaries. Historically, it has evolved from the semiotic and cultural studies of structuralists (Barthes, Kristeva), as well as pioneering Western works on technological mythology (Ryan, Mosco). The former provide a necessary interpretation of myth and mythology, extending it beyond cultural anthropology and history, while the latter demonstrate ways to apply myth interpretation to specific, relevant material. As noted in the previous study, content analysis allowed us to identify some very general formulations of technological mythologemes, which can be further refined and specified. Discussion. The “cloud” representation allowed us to draw statistical conclusions about the peak in time of the topic’s popularity, as well as key concepts related to the original ones. “Transparency” and “artificial intelligence” were used as the original concepts. The graphical expression of intermediate results serves as a systematizing tool for assessing two research parameters: word frequency and semantic connections (the so-called “semantic nest”). Taken together, these indicators can be useful for predicting the development of a “mythological” trend. Initial analysis of the cloud data suggests the discursive position of the “transparency” marker: statistically, it is comparatively rarely used as a subject of analysis or philosophical descriptions and is frequently absorbed into discursive scientific practices. This assumption was demonstrated by references to several scientific research cases, within which “transparency” is explained by uniform “coded” propositions. The key semantic pattern of these propositions is reduced to the figure of the subject and their power. However, as technology develops, the subject at the discursive level finds itself in a contradictory situation.