Ensuring Autonomy of Decision-Making by Artificial Intelligence for the Purposes of Legal Public Relations.

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Abstract

Within the framework of this article, a comparative analysis of existing approaches is carried out to determine the basic conditions for ensuring the autonomy of AI in the context of public legal relations of foreign countries and Russia. As part of the comparative analysis, the basic problems in the field of AI decision-making transparency in world practice, practical situations of integrating non-transparent AI into the sphere of public legal relations in foreign countries, as well as possible compensatory legal measures that ensure the safe integration of AI into the sphere of public administration in Russia are being investigated. The subject of the study is the formalization of the actions of artificial intelligence as a representative of a government body. The object of the research is normative documents, recommendations and other documents regulating the implementation of AI autonomy for the purposes of public legal relations in Russia and foreign countries, judicial practice, academic publications on the issues under study. The research methodology integrates a complex of modern philosophical, general scientific, special scientific methods of cognition, including dialectical, systemic, structural-functional, hermeneutical, comparative legal, formal legal (dogmatic), etc. Within the framework of this study, special emphasis is placed on the implementation of a comparative legal study of the phenomenon of AI autonomy, which implements public functions based on the experience of various states. The measures proposed as a result of the study can be applied in the legislative and law enforcement practice of the relevant authorities implementing the integration of artificial intelligence into the sphere of public relations in Russia.

References

  1. Zwischenbericht der Arbeitsgruppe “Digitaler Neustart” zur Frühjahrskonferenz der Justizministerinnen und Justizminister am 6. und 7. Juni 2018 in Eisenach: [сайт]. — URL: www.justiz.nrw.de/JM/schwerpunkte/digitaler_neustart/zt_fortsetzung_arbeitsgruppe_teil_2/2018-04-23-Zwischenbericht-F-Jumiko-2018%2D%2D-final.pdf (дата обращения: 21.02.2023).
  2. Proposal for a Regulation on promoting fairness and transparency for business users of online intermediation services (COM(2018) 238 final / 2018/0112 (COD)): [сайт]. — URL: https://eur-lex.europa.eu/procedure/EN/2018_112 (дата обращения: 21.02.2023).
  3. The initial proposal (Int. 1696–2017) would have added the text cited above to Section 23-502 of the Administrative Code of the City of New York. However, the law that was finally passed only established a task force which is designated to study how city agencies currently use algorithms: [сайт]. — URL: legistar.council.nyc.gov/LegislationDetail.aspx? ID¼3137815&GUID¼437A6A6D-62E1-47E2-9C42-461253F9C6D0 (дата обращения: 21.02.2023).
  4. Burrell J. How the machine ‘thinks’: Understanding opacity in machine learning algorithms //Big data & society. – 2016. – Т. 3. – №. 1. – С. 2053951715622512.
  5. Ananny M., Crawford K. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability //new media & society. – 2018. – Т. 20. – №. 3. – С. 973-989.
  6. Fenster M. The transparency fix: Secrets, leaks, and uncontrollable government information. – Stanford University Press, 2017.
  7. Grey C., Costas J. Secrecy at work: The hidden architecture of organizational life. – Stanford University Press, 2016.
  8. Мартынов А. В., Бундин М. В. О правовых принципах применения искусственного интеллекта при осуществлении органами исполнительной власти контрольно-надзорной деятельности //Журнал российского права. – 2020. – №. 10. – С. 59-75.
  9. Leese M. The new profiling: Algorithms, black boxes, and the failure of anti-discriminatory safeguards in the European Union //Security Dialogue. – 2014. – Т. 45. – №. 5. – С. 494-511.
  10. Bundesanstalt für Finanzdienstleistungsaufsicht (2018) Big Data trifft auf künstliche Intelligenz. Herausforderungen und Implikationen für Aufsicht und Regulierung von Finanzdienstleistungen: [сайт]. — URL: www.bafin.de/SharedDocs/Downloads/DE/dl_bdai_studie.html (дата обращения: 21.02.2023).
  11. Tutt A. An FDA for Algorithms’(2017) //Administrative law review. – Т. 69. – С. 83.
  12. IBM. Continuous relevancy training: [сайт]. — URL: console.bluemix.net/docs/services/discovery/continu ous-training.html#crt (дата обращения: 21.02.2023).
  13. Hermstrüwer Y. Artificial intelligence and administrative decisions under uncertainty //Regulating Artificial Intelligence. – 2020. – С. 199-223.
  14. Lehr D., Ohm P. Playing with the data: what legal scholars should learn about machine learning //UCDL Rev. – 2017. – Т. 51. – С. 653.
  15. Воробьёва И. Б. Этические аспекты использования систем искусственного интеллекта при расследовании преступлений //Вестник Саратовской государственной юридической академии. – 2022. – №. 4 (147). – С. 162-172.
  16. Харитонова Ю. С., Савина В. С., Паньини Ф. Предвзятость алгоритмов искусственного интеллекта: вопросы этики и права //Вестник Пермского университета. Юридические науки. – 2021. – №. 53. – С. 488-515.
  17. Cowgill B., Tucker C. Algorithmic bias: A counterfactual perspective //NSF Trustworthy Algorithms. – 2017.
  18. Lewis D. Counterfactuals. Harvard University Press. Cambridge, MA. – 1973.
  19. Информация Конституционно-правовая защита предпринимательства: актуальные аспекты (на основе решений Конституционного Суда Российской Федерации 2018-2020 годов) (одобрено решением Конституционного Суда РФ от 17.12.2020)
  20. SyRI legislation in breach of European Convention on Human Rights: [сайт]. — URL: https://www.rechtspraak.nl/Organisatie-en-contact/Organisatie/Rechtbanken/Rechtbank-Den-Haag/Nieuws/Paginas/SyRI-legislation-in-breach-of-European-Convention-on-Human-Rights.aspx (дата обращения 21.02.2023)
  21. District Court of the Hague, 6 March 2020, ECLI:NL:RBDHA:2020:865: [сайт]. — URL: uitspraken.rechtspraak.nl/inziendocument?id=ECLI:NL:RBDHA:2020:1878 (дата обращения 21.02.2023)
  22. MAKING O. F. A. D. PROFILING THE UNEMPLOYED IN POLAND: SOCIAL AND POLITICAL IMPLICATIONS.
  23. Koniec profilowania bezrobotnych: [сайт]. — URL:https://www.prawo.pl/kadry/bezrobotni-nie-beda-profilowani-utrudnialo-to-ich-aktywizacje,394701.html (дата обращения 21.02.2023)
  24. Michigan’s MiDAS Unemployment System: Algorithm Alchemy Created Lead, Not Gold: [сайт]. — URL: https://spectrum.ieee.org/michigans-midas-unemployment-system-algorithm-alchemy-that-created-lead-not-gold#toggle-gdpr (дата обращения 21.02.2023)
  25. Cahoo v. SAS Analytics Inc. Nos. 18-1295/1296: [сайт]. — URL:https://casetext.com/case/cahoo-v-sas-analytics-inc (дата обращения 21.02.2023)

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