UDC 004.89 UDC 519.816

ETHICAL RISKS OF ARTIFICIAL INTELLIGENCE-BASED DECISION SUPPORT SYSTEMS IN ORGANIZATIONS Ethical risks of artificial intelligence-based decision support systems in organizations

Published in National Security and Strategic Planning · Issue 2, 2026 · Pages 5–17 · Rubric: Management of socio-economic systems
DOI: https://doi.org/10.37468/2307-1400-2026-2-5-17
Received: 24.02.2026 Accepted: 14.06.2026 Published: 30.07.2026
This article analyzes the ethical risks arising from the use of artificial intelligence (AI)-based decision support systems in organizations. While these systems improve the speed and validity of decisions by processing large amounts of data, they also raise issues of algorithmic bias, opacity, erosion of responsibility, and privacy protection. Based on a literature review and an analysis of real-world AI case studies, four groups of issues that create ethical risks are identified: bias and fairness issues, transparency and explainability issues, accountability and responsibility in decision-making, and privacy and privacy issues. It is shown that mitigating these risks requires not only compliance with regulatory requirements but also organizational measures (ethical system design, algorithmic audit, the creation of AI ethics committees, and continuous monitoring of models after deployment). The combination of fairness, explainability, and accountability allows organizations to leverage the benefits of AI in decision-making without deviating from ethical standards. The scientific novelty of this study lies in its systematization of the ethical risks of AI-based decision support systems as applied to organizations. The practical significance of this study lies in the potential use of the proposed strategies for the ethical implementation of AI in the design and implementation of decision support systems in organizations.
artificial intelligence, decision support systems, ethical risks, transparency, bias, confidentiality, black box
Text References
Text (PDF)
Read Download

1. Duan Y., Edwards J. S., Dwivedi Y. K. Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda // International Journal of Information Management. – 2019. – Vol. 48. – P. 63–71. – DOIhttps://doi.org/10.1016/j.ijinfomgt.2019.01.021.

2. Shrestha Y. R., Ben-Menahem S. M., von Krogh G. Organizational decision-making structures in the age of artificial intelligence // California Management Review. – 2019. – Vol. 61, No. 4. – P. 66–83. – DOIhttps://doi.org/10.1177/0008125619862257.

3. Matveev A.V. Conceptual Foundations of Implementing Artificial Intelligence in Decision-Making Processes in Organizational Systems // Informatization and Communication. - 2026. - No. 2. - Pp. 28–39. - DOIhttps://doi.org/10.34219/2078-8320-2026-16-2-28-39. - EDN OHCQAJ.

4. Jobin A., Ienca M., Vayena E. The global landscape of AI ethics guidelines // Nature Machine Intelligence. – 2019. – Vol. 1, No. 9. – P. 389–399. – DOIhttps://doi.org/10.1038/s42256-019-0088-2.

5. Marabelli M., Newell S., Handunge V. The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges // The Journal of Strategic Information Systems. – 2021. – Vol. 30, No. 3. – Art. 101683. – DOIhttps://doi.org/10.1016/j.jsis.2021.101683.

6. Mehrabi N., Morstatter F., Saxena N., Lerman K., Galstyan A. A survey on bias and fairness in machine learning // ACM Computing Surveys. – 2021. – Vol. 54, No. 6. – Art. 115. – P. 1–35. – DOIhttps://doi.org/10.1145/3457607.

7. Smirnova A.I. Bias as a problem in AI algorithms: ethical aspects // Philosophy and Society. – 2023. – No. 3(108). – P. 118–126. – DOIhttps://doi.org/10.30884/jfio/2023.03.07. – EDN VNZKTC.

8. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead // Nature Machine Intelligence. – 2019. – Vol. 1, No. 5. – P. 206–215. – DOIhttps://doi.org/10.1038/s42256-019-0048-x.

9. Wachter S., Mittelstadt B., Russell C. Counterfactual explanations without opening the black box: Automated decisions and the GDPR // Harvard Journal of Law & Technology. – 2017. – Vol. 31, No. 2. – P. 841–887.

10. Čartolovni A., Tomičić A., Mosler E. L. Ethical, legal, and social considerations of AI-based medical decision-support tools: a scoping review // International Journal of Medical Informatics. – 2022. – Vol. 161. – Art. 104738. – DOIhttps://doi.org/10.1016/j.ijmedinf.2022.104738.

11. Pesova V.L. Ethics and risks of using AI in financial management: Challenges for small and medium businesses // Construction Economics. - 2025. - No. 3. - Pp. 453-456. - EDN MXTBOG.

12. Mittelstadt B. Principles alone cannot guarantee ethical AI // Nature Machine Intelligence. – 2019. – Vol. 1, No. 11. – P. 501–507. – DOIhttps://doi.org/10.1038/s42256-019-0114-4.

13. Floridi L., Cowls J., Beltrametti M. [et al.] AI4People – An ethical framework for a good AI society: opportunities, risks, principles, and recommendations // Minds and Machines. – 2018. – Vol. 28, No. 4. – P. 689–707. – DOIhttps://doi.org/10.1007/s11023-018-9482-5.

14. Klaus M. Transcending weapon systems: the ethical challenges of AI in military decision support systems // Humanitarian Law & Policy. – 2024. – Vol. 24.

15. Raghavan M., Barocas S., Kleinberg J., Levy K. Mitigating bias in algorithmic hiring: Evaluating claims and practices // Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). – New York: ACM, 2020. – P. 469–481. – DOIhttps://doi.org/10.1145/3351095.3372828.

16. Obermeyer Z., Powers B., Vogeli C., Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations // Science. – 2019. – Vol. 366, No. 6464. – P. 447–453. – DOIhttps://doi.org/10.1126/science.aax2342.

17. Stefan R., Carutasu G. How to approach ethics in intelligent decision support systems // International Symposium in Management Innovation for Sustainable Management and Entrepreneurship. – Cham: Springer International Publishing, 2019. – P. 25–40.

18. Volobuev A.V. Ethics of artificial intelligence, discrimination and inequality // Century of globalization. - 2023. - No. 3. - P. 48–62. – DOIhttps://doi.org/10.30884/glob/2023.03.04.

19. Barredo Arrieta A., Díaz-Rodríguez N., Del Ser J. [et al.] Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI // Information Fusion. – 2020. – Vol. 58. – P. 82–115. – DOIhttps://doi.org/10.1016/j.inffus.2019.12.012.

20. Selbst A. D., Boyd D., Friedler S. A., Venkatasubramanian S., Vertesi J. Fairness and abstraction in sociotechnical systems // Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* ’19). – New York: ACM, 2019. – P. 59–68. – DOIhttps://doi.org/10.1145/3287560.3287598.

21. Amann J., Blasimme A., Vayena E., Frey D., Madai V. I. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective // BMC Medical Informatics and Decision Making. – 2020. – Vol. 20. – Art. 310. – DOIhttps://doi.org/10.1186/s12911-020-01332-6.

22. Raji I. D., Smart A., White R. N. [et al.] Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing // Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20). – New York: ACM, 2020. – P. 33–44. – DOIhttps://doi.org/10.1145/3351095.3372873.

23. Adadi A., Berrada M. Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI) // IEEE Access. – 2018. – Vol. 6. – P. 52138–52160. – DOIhttps://doi.org/10.1109/ACCESS.2018.2870052.

24. Matveev A.V. Comparative analysis of methods for interpretability of artificial intelligence models in decision-making processes // Modern science-intensive technologies. - 2026. - No. 5. - P. 131–138. - DOIhttps://doi.org/10.17513/snt.40785. - EDN KKJORY.

25. Ribeiro M. T., Singh S., Guestrin C. “Why should I trust you?”: Explaining the predictions of any classifier // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. – New York: ACM, 2016. – P. 1135–1144. – DOIhttps://doi.org/10.1145/2939672.2939778.

26. Lundberg S. M., Lee S.-I. A unified approach to interpreting model predictions // Advances in Neural Information Processing Systems. – 2017. – Vol. 30. – P. 4765–4774.

27. Apostolova N. N. Liability for harm caused by artificial intelligence // North Caucasian Legal Bulletin. – 2021. – No. 1. – P. 112–119. – DOIhttps://doi.org/10.22394/2074-7306-2021-1-1-112-119.

28. Rezaei M., Pironti M., Quaglia R. AI in knowledge sharing, which ethical challenges are raised in decision-making processes for organisations? // Management Decision. – 2025. – Vol. 63, No. 10. – P. 3369–3388.

29. Davydov E.V., Snogovskaya Yu.O. Ethical and legal aspects of using AI in expert activities // Law and Management. – 2026. – No. 2. – P. 462–465. – DOIhttps://doi.org/10.24412/2224-9133-2026-2-462-465. – EDN YDVYFY.

30. Mikalef P., Conboy K., Lundström J. E., Popovič A. Thinking responsibly about responsible AI and ‘the dark side’ of AI // European Journal of Information Systems. – 2022. – Vol. 31, No. 3. – P. 257–268. – DOIhttps://doi.org/10.1080/0960085X.2022.2026621.

31. Biondi G. [et al.] Ethical design of artificial intelligence-based systems for decision making // Frontiers in Artificial Intelligence. – 2023. – Vol. 6. – Art. 1250209. – DOIhttps://doi.org/10.3389/frai.2023.1250209.

32. McMahan H. B., Moore E., Ramage D., Hampson S., Agüera y Arcas B. Communication-efficient learning of deep networks from decentralized data // Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). – PMLR, 2017. – Vol. 54. – P. 1273–1282.

33. Mökander J., Axente M., Casolari F., Floridi L. Conformity assessments and post-market monitoring: a guide to the role of auditing in the proposed European AI regulation // Minds and Machines. – 2022. – Vol. 32, No. 2. – P. 241–268. – DOIhttps://doi.org/10.1007/s11023-021-09577-4.

34. Hagendorff T. The ethics of AI ethics: An evaluation of guidelines // Minds and Machines. – 2020. – Vol. 30, No. 1. – P. 99–120. – DOIhttps://doi.org/10.1007/s11023-020-09517-8.

35. Christiano P. F., Leike J., Brown T. B., Martic M., Legg S., Amodei D. Deep reinforcement learning from human preferences // Advances in Neural Information Processing Systems. – 2017. – Vol. 30. – P. 4299–4307.

36. Berman N.D. Overcoming the Challenges of Implementing AI Agents in the Educational Environment: Focus on Ethical Aspects and Risks // CITISE. - 2025. - No. 3 (45). - Pp. 166-177. - EDN MFAQIU.

37. Alborova, M.B. § 3. Ethical Aspects of AI: Recommendation of UNESCO and Other Organizations // International Security in the Age of Artificial Intelligence: Textbook for Universities. - 2nd ed., corrected and enlarged. - Moscow: Aspect Press Publishing House, 2025. - Pp. 439-457. - EDN ZEYFVO.

38. OECD. Recommendation of the Council on Artificial Intelligence. – OECD/LEGAL/0449. – Paris: OECD, 2019.