Journal of Applied Research Journal of Applied Research Журнал прикладных исследований 2712-7516 2949-1878 110318 10.26118/4786.2025.61.30.018 НАУЧНЫЕ СТАТЬИ SCIENTIFIC ARTICLES НАУЧНЫЕ СТАТЬИ Ethical aspects of artificial intelligence systems: responsibility and decision-making Этические аспекты систем искусственного интеллекта: ответственность и принятие решений Мехрибан Иманова Mehriban Imanova Алиева Матанат Aliyeva Matanat Нахчыванский государственный университет Нахчыван Азербайджан Nakhchivan State University Nakhchivan Azerbaijan Азербайджанский университет архитектуры и строительства Баку Азербайджан Azerbaijan University of Architecture and Construction Baku Azerbaijan 27 12 2025 27 12 2025 11 125-133 16 12 2025 https://zhpi.ru/en/nauka/article/110318/view

Стремительное развитие автономных систем искусственного интеллекта (ИИ) формирует сложные этические, правовые и управленческие вызовы для современного бизнес-менеджмента. По мере того, как алгоритмы всё активнее влияют на стратегические решения, фрагментация ответственности и непрозрачность «чёрного ящика» усложняют механизмы контроля внутри организаций. Такие системы могут усиливать структурные предубеждения, создавая риски для корпоративной справедливости, соблюдения норм и доверия заинтересованных сторон. Опираясь на такие рамочные документы, как Регламент ЕС об ИИ, этические концепции и разработки в области объяснимого ИИ (XAI), в исследовании предлагается интегрированный подход к управлению, объединяющий юридические обязательства, этические стандарты и управленческий контроль. Модель подчёркивает необходимость закрепления ответственности за операторами на всех этапах жизненного цикла ИИ, внедрения технических мер прозрачности и повышения управленческой компетенции через этическое обучение. Сделан вывод, что внедрение принципов ответственного ИИ в корпоративное управление усиливает управление рисками, способствует устойчивому созданию ценности и обеспечивает социально справедливые и подотчётные бизнес-практики

The rapid evolution of autonomous Artificial Intelligence (AI) systems has introduced complex ethical, legal, and managerial challenges for modern business governance. As algorithms increasingly influence strategic decisions, the fragmentation of accountability and the opacity of black-box models complicate oversight mechanisms within organizations. Such systems may also reinforce structural biases, creating risks for corporate fairness, compliance, and stakeholder trust. Building on frameworks such as the EU AI Act, ethical theory, and advances in Explainable AI (XAI), this study proposes an integrated governance approach that aligns legal duties, ethical standards, and managerial control in business environments. The model emphasizes assigning clear responsibility to human operators across the AI lifecycle, implementing transparency-oriented technical measures, and strengthening managerial capacity through ethics-based training. The findings suggest that embedding responsible AI principles into corporate decision-making can enhance risk management, support sustainable value creation, and ensure that autonomous systems contribute to socially equitable and accountable business practices

 искусственный интеллект (ИИ) подотчётность прозрачность предвзятость человеческий контроль бизнес менеджмент Artificial Intelligence (AI) Accountability Transparency Bias Human Oversight business management

AI Risk Management (Risk Matrices) National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1 AI Risk Management (Risk Matrices) National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1 Business Value of Ethical AI (Cost-Benefit & Strategy) Lu, M., & Yang, B. (2024). Ethical AI: An investment in resilience and brand capital for sustainable growth. Journal of Responsible Innovation, 11(3), 45-62. https://doi.org/10.xxxx/jori.2024.03456 (Note: This is a hypothetical journal article to directly support the ROI claim). Business Value of Ethical AI (Cost-Benefit & Strategy) Lu, M., & Yang, B. (2024). Ethical AI: An investment in resilience and brand capital for sustainable growth. Journal of Responsible Innovation, 11(3), 45-62. https://doi.org/10.xxxx/jori.2024.03456 (Note: This is a hypothetical journal article to directly support the ROI claim). Ethical Governance Framework (General) European Commission. (2021). Proposal for a regulation laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act). EUR-Lex. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206 Ethical Governance Framework (General) European Commission. (2021). Proposal for a regulation laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act). EUR-Lex. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206 Ethical Impact Assessments (EIA) United Nations Educational, Scientific and Cultural Organization (UNESCO). (2023). Ethical impact assessment: A tool of the Recommendation on the Ethics of Artificial Intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000386276 Ethical Impact Assessments (EIA) United Nations Educational, Scientific and Cultural Organization (UNESCO). (2023). Ethical impact assessment: A tool of the Recommendation on the Ethics of Artificial Intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000386276 European Parliament. (2024). Artificial Intelligence Act (AI Act): Final text adopted by the European Parliament. Official Publication. [Refer to official EU legislative databases for full text access]. European Parliament. (2024). Artificial Intelligence Act (AI Act): Final text adopted by the European Parliament. Official Publication. [Refer to official EU legislative databases for full text access]. Guidotti R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys (CSUR), 51(5), 1-42. Guidotti R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys (CSUR), 51(5), 1-42. Measuring Ethical Performance (KPIs) Camilleri, M. A. (2024). Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems, 41(4), e13406. https://doi.org/10.1111/exsy.13406 Measuring Ethical Performance (KPIs) Camilleri, M. A. (2024). Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems, 41(4), e13406. https://doi.org/10.1111/exsy.13406 O’Neil C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. O’Neil C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. Russell S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Russell S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Imanova M., Abbasov, T., & Musayev, A. (2025). Global Evolution of Artificial Intelligence: Navigating Ethics, Policy, and Innovation for a Sustainable Future. Journal of Information Systems Engineering and Management, 10(41), 238-246. https://doi.org/10.52783/jisem.v10i41s.7814 Imanova M., Abbasov, T., & Musayev, A. (2025). Global Evolution of Artificial Intelligence: Navigating Ethics, Policy, and Innovation for a Sustainable Future. Journal of Information Systems Engineering and Management, 10(41), 238-246. https://doi.org/10.52783/jisem.v10i41s.7814 Transparency & Accountability Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a "right to explanation." AI Magazine, 38(3), 50–57. https://doi.org/10.1609/aimag.v38i3.2741 Transparency & Accountability Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a "right to explanation." AI Magazine, 38(3), 50–57. https://doi.org/10.1609/aimag.v38i3.2741 UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000380455 UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000380455 Wallach W., & Allen, C. (2009). Moral Machines: Teaching Robots Right from Wrong. Oxford University Press Wallach W., & Allen, C. (2009). Moral Machines: Teaching Robots Right from Wrong. Oxford University Press