PROSPECTS OF ARTIFICIAL INTELLIGENCE USE IN ANTI-CORRUPTION POLICY

  • Olga V Pavlova Russian Presidential Academy of National Economy and Public Administration under the President of the Russian Federation, Rostov-on-Don
  • Anamika P Singkh Russian Presidential Academy of National Economy and Public Administration under the President of the Russian Federation, Rostov-on-Don
Keywords: Artificial Intelligence, anti-corruption policy, neural network, corruption, crime, technology

Abstract

In the paper, authors examine the prospects for the artificial intelligence introduction in anti-corruption policy, based on foreign experience, and also identify the difficulties of using such technology in Russia. This article discusses the possible risks when using Artificial Intelligence, as well as the specifics of Russian legislation in the field of information space regulation.

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Author Biographies

Olga V Pavlova, Russian Presidential Academy of National Economy and Public Administration under the President of the Russian Federation, Rostov-on-Don

BBA, faculty of economics, South Russian Institute of Management Russian Presidential Academy of Economy and Public Administration (RANEPA), Rostov-on-Don, Russia

Research interests: global economics, multinational business, governance.

Anamika P Singkh, Russian Presidential Academy of National Economy and Public Administration under the President of the Russian Federation, Rostov-on-Don

BBA, faculty of economics, South Russian Institute of Management Russian Presidential Academy of Economy and Public Administration (RANEPA), Rostov-on-Don, Russia

Research interests: international business, management, innovative economics.

References

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Federal Law (1990). N 395-1 “On Banks and Banking Activities”.

Federal Law (1995). N 144-FZ “On Operative-Investigative Activities”.

General Prosecutor's Office of the Russian Federation. Available online: http://crimestat.ru/analytics

Hlatshwayo, S., Oeking, A., Ghazanshyan, M., Corvino, D., Shukla, A. & Leigh, L.Y. (2018). The Measurement and Macro-Relevance of Corruption: A Big Data Approach. International Monetary Fund Working Papers. 18/195.

Larina, O. I. & Akimov, O. M. (2020). Digital money today: key risks and development trends. Finance: Theory and Practice, 24(4), 18-30 [in Russian].

United Nations Office on Drugs and Crime. Available online: https://aiforgood.itu.int/about/un-ai-actions/unov-unodc/

Voronczov, S., Ovchinnikov, A., Mamy`chev, A., Kravchenko, A. & Senik, A. (2021). Anti-corruption in the digitalization of the state, law and economy: conceptual and institutional aspects. Taganrog: Southern Federal University Press [in Russian].

World Bank Blogs: «Can artificial intelligence stop corruption in its tracks?». Available online: https://blogs.worldbank.org/governance/can-artificial-intelligence-stop-corruption-its-tracks

Abstract views: 90
PDF Downloads: 83
Published
2022-03-31
How to Cite
Pavlova, O., & Singkh, A. (2022). PROSPECTS OF ARTIFICIAL INTELLIGENCE USE IN ANTI-CORRUPTION POLICY. The EUrASEANs: Journal on Global Socio-Economic Dynamics, (2(33), 60-65. https://doi.org/10.35678/2539-5645.2(33).2022.60-65