Образование и право2076-150310.24412/2076-1503-2026-2-180-188OtherКОНЦЕПЦИЯ ГРАДУИРОВАННОЙ АЛГОРИТМИЧЕСКОЙ ПРОЗРАЧНОСТИ В КОНТРОЛЬНО-НАДЗОРНОЙ ДЕЯТЕЛЬНОСТИ: ТЕОРИЯ И МЕХАНИЗМЫ РЕАЛИЗАЦИИTHE CONCEPT OF GRADUATED ALGORITHMIC TRANSPARENCY IN CONTROL AND SUPERVISORY ACTIVITIES: THEORY AND IMPLEMENTATION MECHANISMSФроловАлександр АлександровичFrolovAlexander Aleksandrovichafrolov77@yandex.ruhttps://orcid.org/ORCID: 0009-0008-7131-1785Postgraduate student, Department of State and Municipal Management, National Research Mordovian State University, named after N.P. Ogarev, Saransk, Russia, Head of the Legal Service of JSC “Orbita”, Saransk, Russiaаспирант кафедры государственного и муниципального управления, Национальный исследовательский Мордовский государственный, университет им. Н.П. Огарева, Саранск, Россия, руководитель юридической службы АО «Орбита», Саранск, Россия1003202621801882801202610032026

Цифровизация контрольно-надзорной деятельности (КНД) в Российской Федерации создаёт вызовы по обеспечению прозрачности алгоритмических систем при защите критически важной информации. Традиционный бинарный подход — полное раскры тиелибо полная секретность — не отвечает потребностям различных заинтересован ныхсторон. Статья развивает концепцию градуированной алгоритмической прозрачно сти, адаптированную к российским условиям КНД на основе международного опыта (Вели кобритания, Нидерланды, Новая Зеландия, Европейский союз). Предложена трёхуровневая модель доступа к информации об алгоритмических системах: публичная прозрачность (для всех граждан), специализированная прозрачность (для надзорных органов и экспертов) и полная техническая прозрачность (для государственных регуляторов). Разработана система из 20 механизмов реализации: нормативно-правовых (проект статьи 55.1 ФЗ № 248), организационно-технических (государственный реестр алгоритмических систем), процедурных, технологических (XAI, audit trails, model cards) и институциональных. Иссле дование вносит вклад в развитие правовой доктрины цифрового государственного управ ления и предлагает практические инструменты для совершенствования законодатель ства о КНД

The digitalization of control and supervisory activities (CSA) in the Russian Federation creates challenges in ensuring transparency of algorithmic systems while protecting critical information. The traditional binary approach—full disclosure or complete secrecy—does notmeet the needs of various stakeholders. The article develops the concept of graduated algorithmic trans

алгоритмическая прозрачностьконтрольно-надзорная деятель ностьискусственный интеллектцифровизация государственного управлениягосудар ственный реестр алгоритмовобъяснимый ИИградуированный доступответственное регулированиеподотчётностьalgorithmic transparencycontrol and supervisory activitiesartificial intelligencedigitalization of public administrationgovernment algorithm registerexplainable AIgraduated accessresponsible regulationaccountabilityTalesh S. A. Regulating Algorithmic Discrimi nation: An Accountability Approach // Yale Journal on Regulation. 2018. Vol. 35. No. 2. P. 95–112.Yeung K. Hypernudges: Private Digital Governance in the Data-Driven Society // Harvard Journal of Law & Technology. 2017. Vol. 32. No. 1. P. 123– 151.Citron D. K., Pasquale F. The Scored Soci ety: DueProcess for Automated Predictions // Wash ington LawReview. 2014. Vol. 89. No. 1. P. 780–810.Barocas S., Selbst A. D. BigData’s Dispa rate Impact // California LawReview. 2016. Vol. 104. 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No. 2. P. 95–112.Yeung K. Hypernudges: Private Digital Governance in the Data-Driven Society // Harvard Journal of Law & Technology. 2017. Vol. 32. No. 1. P. 123– 151.Citron D. K., Pasquale F. The Scored Soci ety: DueProcess for Automated Predictions // Wash ington LawReview. 2014. Vol. 89. No. 1. P. 780–810.Barocas S., Selbst A. D. BigData’s Dispa rate Impact // California LawReview. 2016. Vol. 104. P. 671–732.Brundage M., Anderljung M., Wang L., Garfinkel B., Andersson Y., Andersson C. Artificial Intelli gence, Systemic Risk, and Scenario Simulation // arXiv preprint arXiv:2306.00326. 2023.Selbst A. D., Barocas S., Braman J., Givone M., Nissenbaum H. Fairness and Abstraction in Sociotechnical Systems // Proceedings of the Confer ence on Fairness, Accountability, and Transparency. PMLR. 2019. P. 59–68.Ressйguier A., Rodrigues R. AIEthics Should NotRemain Toothless! // International Data Privacy Law. 2020. Vol. 10. No. 2. P. 120–140.Mittelstadt B. 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Algorithmic Transparency and Accountability. Cham: Springer. 2019.Richardson R., Schultz J. M., Crawford K. Dirty Data, BadPredictions: HowCivil Rights Viola tions Impact Police Data, Predictive Policing Systems, and Justice // NewYork University Journal of Legislation and Public Policy. 2019. Vol. 23. P. 1043– 1074.Fjeld J., Achten N., Hilligoss H., Nagy A. C., Srikumar M. Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to AIGovernance. Berkman Klein Center Research Publication No. 2020-1. 2020.Ressйguier A., Rodrigues R. AIEthics Should NotRemain Toothless! // International Data Privacy Law. 2020. Vol. 10. No. 2. P. 120–140.Morley J., Machado C. C. V., Burr C., Cowls J., Jansen I., Taddeo M., Floridi L. From What to How: An Initial Review of Publicly Available AIEthics Tools, Methods and Research to Translate Principles into Practices // Science and Engineering Ethics. 2020. Vol. 26. No. 4. P. 2141–2168.Selbst A. D., Barocas S. 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Public Service AIFramework. Wellington: Department of Internal Affairs. 2025.European Parliament. Regulation (EU) 2024/1689 on Artificial Intelligence (AI Act). 2024.Global Partnership on AI. Guardrails for AI: Introducing the Framework. Paris: GPAI. 2024.Leavy B., O’Sullivan B., Siapera E. Data, Power and Bias in Artificial Intelligence // arXiv preprint arXiv:2108.07341. 2021.Ribeiro M. T., Singh S., Guestrin C. “WhyShould I Trust You?”: Explaining the Predictions of AnyClassifier // Proceedings of the 22nd ACM SIG KDDInternational Conference on Knowledge Discov ery and Data Mining. 2016. P. 1135–1144.Shrikumar A., Alexandari A., Poursalimi A., Hoffman J., Ermon S., Xing E. P. Learning Important Features Through Propagating Activation Differences // Proceedings of the 36th International Conference on Machine Learning. PMLR. 2019. P. 5191–5199.Mitchell M., Wu S., Zaldivar A., Barnes P., Vasserman L., Hutchinson B., et al. Model Cards for Model Reporting // Proceedings of the Conference on Fairness, Accountability, and Transparency. PMLR. 2019. P. 220–229.USDepartment of Commerce. National Institute of Standards and Technology (NIST) AIRisk Management Framework. Version 1.0. Gaithersburg, MD: NIST. 2023.Information Technology Industry Council (ITI). AIAccountability Framework. Washington, D.C.: ITI. 2021.Eubanks V. Automating Inequality: HowHigh-Tech Tools Profile, Police, and Punish the Poor. NewYork: St. Martin’s Press. 2018.Goodfellow I., Papernot N., McDaniel P. Explaining and Harnessing Adversarial Examples // arXiv preprint arXiv:1412.6572. 2014.Bolukbasi T., Chang K. W., Zou J. Y., Saligrama V., Kalai A. T. Man is to Computer Programmer asWoman is to Homemaker? Debiasing Word Embeddings // Advances in Neural Information Processing Systems. 2016. P. 4349–4357.Hardt M., Price E., Srebro N. Equality of Opportunity in Supervised Learning // Advances in Neural Information Processing Systems. 2016. P. 3315–3323.Buolamwini J., Gebru T. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification // Conference on Fairness, Accountability and Transparency. PMLR. 2018. P. 77–91.