- Issue
- Journal of Siberian Federal University. Humanities & Social Sciences. 2021 14 (11)
- Authors
- Minbaleev, Aleksey V.; Evsikov, Kirill S.
- Contact information
- Minbaleev, Aleksey V.: Kutafin Moscow State Law University (MSAL) Moscow, Russian Federation; ; ORCID: 0000-0001-5995-1802; Evsikov, Kirill S.: Kutafin Moscow State Law University (MSAL) Moscow, Russian Federation; Tula State University Tula, Russian Federation; ; ORCID: 0000-0002-4593-0063
- Keywords
- anti-corruption; information technology; big data; artificial intelligence
- Abstract
Most countries are making significant efforts to combat corruption. International organizations have developed effective recommendations that have allowed many states to achieve success in the implementation of anti-corruption policies. Using these recommendations Russian Government has developed and implemented effective methods for combating this social phenomenon. Currently, the results obtained from anti-corruption activities are declining. Having considered the tendencies in the development of anti-corruption mechanisms in Russia and worldwide, the authors concluded that there is a delayed decrease in efficiency from the use of anti-corruption methods. In particular, the method of transparency in the long-term period leads to the complication of relations between the corruption interaction subjects, instead of corruption neutralization. To overcome the effect of the delayed decrease in efficiency, the authors put forward a hypothesis about the need to introduce big data processing technologies and artificial intelligence into the anti-corruption system. The work analyzes the foreign experience of using these tools. Based on the results of the analysis, the authors identified problems encountered by foreign specialists and gave recommendations on the organization of anti-corruption activity in Russia. The article proposes the author’s structure of an artificial intelligence system that carries out a comprehensive anti-c
- Pages
- 1674–1689
- DOI
- 10.17516/1997-1370-0849
- Paper at repository of SibFU
- https://elib.sfu-kras.ru/handle/2311/144846
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).