Везде
Ensuring Data Quality in Automated Decision-Making in Public Administration
Table of contents
Annotation Estimate Publication content
References Comments
Share
QR
Metrics
Ensuring Data Quality in Automated Decision-Making in Public Administration
Annotation
PII
S1605-65900000622-5-1
DOI
10.61205/S160565900029696-9
Publication type
Article
Status
Published
Authors
Nikita Nazarov 
Occupation: Senior Specialist
Affiliation: Institute of Legislation and Comparative Law under the Government of the Russian Federation
Address: Moscow, Russia
Edition
Volume 28 No. 5
Pages
140-155
Abstract

Russian public administration is characterized by a tendency to expand the use of automated decision-making systems that provide partial or complete automation of the procedure for making law enforcement decisions by executive authorities. The application and use of such systems is carried out in the absence of a holistic regulation that ensures the legality, transparency and validity of automated solutions and establishes additional legal guarantees for citizens and organizations.

Objectives and purposes of the study: to define a system of legal requirements that contribute to ensuring data quality in automated decision-making in public administration.

Research methods: general and special methods, including the dialectical method of scientific cognition, system-structural, formal legal, comparative legal methods, methods of analysis, synthesis, comparison and generalization.

Conclusions: one of the central elements of the legal regime for the functioning of automated decision-making systems in public administration should be the legislative establishment of data quality requirements, which are proposed to be understood as a set of requirements for the properties of data (datasets) and requirements for procedures for their processing and use. A system of data quality requirements and the specifics of its application in various fields of activity of executive authorities, including the requirements, is proposed: 1) reliability (accuracy, completeness and relevance) and consistency of data; 2) non-discrimination (representativeness) of data; 3) verifiability of data; 4) confidentiality of data; 5) adequacy and targeted limitations of data collection; 6) openness of data sets within the limits that do not violate confidentiality.

Keywords
automated decision-making, automated decision, data quality, information, data, transparency, government, black box, artificial intelligence, machine learning
Date of publication
17.05.2024
Number of purchasers
2
Views
91
Readers community rating
0.0 (0 votes)
Cite Download pdf

To download PDF you should pay the subscribtion
1

References



Additional sources and materials

1. Tikhomirov Yu. A. Management decision. Moscow, 1972. 288 p. (In Russ.) 
2. Khabrieva T. Y. Technological Imperatives of the Modern World and the Role of Law. Journal of Foreign Legislation and Comparative Law, 2023, vol. 19, no. 1, pp. 5—12. (In Russ.) DOI: 10.12737/jzsp.2023.001. 
3. Talapina E. V. Automated Decision-Making in Public Administration: A Theoretical Analysis of Possibilities. Journal of Russian Law, 2022, vol. 26, no. 11, pp. 60—73. (In Russ.) DOI: 10.12737/jrl.2022.117. 
4. Amelin R. V., Channov S. E. The evolution of law under the impact of digital technologies. Moscow, 2023. 280 p. (In Russ.) 
5. Povetkina N. A., Kopina A. A. Digital rights of the taxpayer. Nalogi, 2020, no. 5, pp. 40— 44. (In Russ.) 
6. Sinitsyn S. A. Personal Moral Rights and Human Security in the Virtual Space. Journal of Foreign Legislation and Comparative Law, 2023, vol. 19, no. 1, pp. 13—24. (In Russ.) DOI: 10.12737/jzsp.2023.002. 
7. Sousa W.G.D., Melo E.R.P.D. et al. How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 2019, vol. 36, no 4. DOI: 10.1016/j.giq.2019.07.004. 
8. Sharma G. D., Yadav A., Chopra R. Artificial intelligence and effective governance: A review, critique and research agenda. Sustainable Futures, 2020, vol. 2. DOI: 10.1016/j. sftr.2019.100004. 
9. Margetts H., Dorobantu C. Rethink government with AI. Nature, 2019, vol. 568, no. 7751. DOI: 10.1038/d41586-019-01099-5. 
10. Krokhina Yu. A. Errors of digital technologies in tax control: Legal consequences for the budget system. Finansovoe pravo, 2022, no. 9. (In Russ.) DOI: 10.18572/1813-1220-2022-9-26-29. 
11. Petrovskaya O. V. The principle of reliability in information law. Cand. diss. Moscow, 2022, 204 p. (In Russ.) 
12. Amelin R. V. Presumption of information reliability in state information systems. Izvestiya Saratovskogo universiteta. Novaya seriya. Seriya: Ekonomika. Upravlenie. Pravo, 2017, vol. 17, no. 4. (In Russ.) 
13. Carey P. Data protection: a practical guide to UK and EU law. Data protection. 5th ed. Oxford, 2018. 369 p. 
14. Edwards L., Veale M. Enslaving the Algorithm: From a “Right to an Explanation” to a “Right to Better Decisions”? IEEE Security and Privacy, 2018, vol. 16, no. 3, pp. 46—54. DOI: 10.1109/MSP.2018.2701152. 
15. Barocas S., Selbst A. D. Big Data's Disparate Impact. California Law Review, 2016, vol. 104, no. 3, pp. 671—732. DOI: 10.15779/Z38BG31. 
16. Goodman B., Flaxman S. European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”. AI Magazine, 2017, vol. 38, no. 3. DOI: 10.1609/aimag.v38i3.2741. 
17. Clemmensen L. H., Kjærsgaard R. D. Data Representativity for Machine Learning and AI Systems, 2022. DOI: 10.48550/ARXIV.2203.04706. 
18. Bratus' S. N., Petrov I. N., Vengerov A. B. et al. Soviet legislation and economic mechanism. Moscow, 1984. (In Russ.) 
19. Zuiderveen Borgesius F. J. Singling out people without knowing their names — Behavioural targeting, pseudonymous data, and the new Data Protection Regulation. Computer Law and Security Review, 2016, vol. 32, no. 2. DOI: 10.1016/j.clsr.2015.12.013. 
20. Bradford A. The Brussels Effect: How the European Union Rules the World. Oxford, 2020. 
21. Saf'yannikov A. V. Depersonalized Data: Is There a Future for Russia? Vestnik ekonomicheskogo pravosudiya Rossiyskoy Federatsii, 2022, vol. 17, no. 9, pp. 55—74. (In Russ.) 
22. Kuner C., Bygrave L. A., Docksey C. The EU General Data Protection Regulation (GDPR): a commentary. The EU General Data Protection Regulation (GDPR). Oxford, 2019.
23. Rocher L., Hendrickx J. M., Montjoye Y.-A. de. Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications, 2019, vol. 10, no 1. DOI: 10.1038/s41467-019-10933-3. 
24. Edidin B. A., Krymskaya K. V. Collection of redundant data: issues of legal regulation. Pravo i tsifrovaya ekonomika, 2022, no. 2, pp. 27—32. (In Russ.) DOI: 10.17803/2618- 8198.2022.16.2.027-032. 
25. Tereshchenko L. K. Access to Information: Legal Guarantees. Journal of Russian Law, 2010, no. 10, pp. 46—53. (In Russ.) 
26. Adadi A. A survey on data-efficient algorithms in big data era. Journal of Big Data, 2021, vol. 8, no. 1, pp. 1—54. DOI: 10.1186/s40537-021-00419-9.

Comments

No posts found

Write a review
Translate