Otonom Sistemlerde Algoritmik İnisiyatifin Yönetimi: Kurumsal Hesap Verebilirlik İçin Bir Yönetişim Çerçevesi

Abstract

Özerk ve etken yapay zekâ sistemleri, bilgi teknolojisini pasif bir otomasyon aracından etkin bir karar verme vekiline dönüştürüyor. Yapay zekâ uyarlanabilir ve öngörülemez kararlar aldığında, geleneksel insan merkezli sorumluluk modelleri yetersiz kalıyor. Bu kavramsal çalışma, stratejik amaçların algoritmik aktörlere devredilmesiyle ortaya çıkan hesap verebilirlik boşluğunu ele alıyor. Üç vakadan (Uber otonom araç kazası, 2010 Flash Crash ve COMPAS) yola çıkarak Dinamik Yetki Devri Modeli (DADM) geliştirilmektedir. Model, sorumluluğu insan stratejik niyeti, algoritmik operasyonel uygulama ve kurumsal gözetim katmanları arasında dağıtır ve sorumlu yapay zekâ yönetişimi için uygulanabilir bir çerçeve sunar.

Keywords

• Özerk Sistemler
• Yapay Zekâ Yönetişimi
• Bilgi Teknolojisi Yönetimi
• Kurumsal Hesap Verebilirlik
• Algoritmik Hesap Verebilirlik

Supporting Institution

Bu çalışma; kamu, ticari veya kâr amacı gütmeyen herhangi bir kurum ya da kuruluştan özel bir finansal destek almamıştır.

Project Number

Yoktur

Ethical Statement

Katılımcılardan doğrudan veri toplanmamış ve kişisel ya da hassas bilgi işlenmemiştir. Bu nedenle etik kurul onayı gerektirmemektedir.

Managing Algorithmic Initiative in Autonomous Systems: A Governance Framework for Corporate Accountability

Abstract

Autonomous and agentic AI systems are turning information technology from a passive automation tool into an active decision-making proxy. Traditional human-centered liability models fall short once AI makes adaptive, high-impact decisions. This conceptual study analyzes the accountability gap that opens when strategic goals are delegated to algorithmic agents. Drawing on three cases (the Uber autonomous vehicle accident, the 2010 Flash Crash, and the COMPAS judicial risk assessment system), it develops the Dynamic Authority Delegation Model (DADM), which distributes responsibility among human strategic intent, algorithmic operational execution, and institutional oversight. By moving from individual blame to organizational governance, the study contributes to the IT management literature and offers a practical framework for corporate accountability, human oversight, algorithmic auditing, and responsible AI governance.

Keywords

• Autonomous Systems
• AI Governance
• Information Technology Management
• Corporate Accountability
• Algorithmic Accountability

Supporting Institution

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Project Number

Yoktur

Ethical Statement

No data were collected directly from human participants, and no personal or sensitive information was processed. Therefore, ethics committee approval was not required.

References

1. Acemoglu, D. & Johnson, S. (2023) Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity, New York: PublicAffairs. ISBN 978-1-5417-0253-0. https://www.hachettebookgroup.com/titles/daron-acemoglu/power-and-progress/9781541702554/
2. Amodei, D.; Olah, C.; Steinhardt, J.; Christiano, P.; Schulman, J. & Mané, D. (2016) "Concrete Problems in AI Safety", arXiv preprint. https://doi.org/10.48550/arXiv.1606.06565
3. Angwin, J.; Larson, J.; Mattu, S. & Kirchner, L. (2016) "Machine Bias: There's Software Used Across the Country to Predict Future Criminals. And It's Biased Against Blacks", ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Awad, E.; Dsouza, S.; Kim, R.; Schulz, J.; Henrich, J.; Shariff, A.; Bonnefon, J.-F. & Rahwan, I. (2018) "The Moral Machine Experiment", Nature, 563(7729), s. 59-64. https://doi.org/10.1038/s41586-018-0637-6
4. Bainbridge, L. (1983) "Ironies of Automation", Automatica, 19(6), s. 775-779. https://doi.org/10.1016/0005-1098(83)90046-8
5. Bandura, A. (2001) "Social Cognitive Theory: An Agentic Perspective", Annual Review of Psychology, 52(1), s. 1-26. https://doi.org/10.1146/annurev.psych.52.1.1
6. Beer, J. M.; Fisk, A. D. & Rogers, W. A. (2014) "Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction", Journal of Human-Robot Interaction, 3(2), s. 74-99. https://doi.org/10.5898/JHRI.3.2.Beer
7. Bender, E. M.; Gebru, T.; McMillan-Major, A. & Shmitchell, S. (2021) "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?", Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, s. 610-623. https://doi.org/10.1145/3442188.3445922
8. Beurer-Kellner, L.; Buesser, B.; Creţu, A.-M. et al. (2025) "Design Patterns for Securing LLM Agents against Prompt Injections", arXiv preprint. https://doi.org/10.48550/arXiv.2506.08837

9. Bostrom, N. & Yudkowsky, E. (2014) "The Ethics of Artificial Intelligence", iç. The Cambridge Handbook of Artificial Intelligence, (Ed.) K. Frankish & W. M. Ramsey, Cambridge: Cambridge University Press. s. 316-334. https://doi.org/10.1017/CBO9781139046855.020
10. Bradshaw, J. M.; Hoffman, R. R.; Woods, D. D. & Johnson, M. (2013) "The Seven Deadly Myths of 'Autonomous Systems'", IEEE Intelligent Systems, 28(3), s. 54-61. https://doi.org/10.1109/MIS.2013.70
11. Brynjolfsson, E. & McAfee, A. (2017) "The Business of Artificial Intelligence", Harvard Business Review. https://hbr.org/2017/07/the-business-of-artificial-intelligence
12. Burrell, J. (2016) "How the Machine 'Thinks': Understanding Opacity in Machine Learning Algorithms", Big Data & Society, 3(1), s. 1-12. https://doi.org/10.1177/2053951715622512
13. Calo, R. (2017) "Artificial Intelligence Policy: A Primer and Roadmap", UC Davis Law Review, 51(2), s. 399-435. https://lawreview.law.ucdavis.edu/archives/51/2/artificial-intelligence-policy-primer-and-roadmap
14. Cath, C. (2018) "Governing Artificial Intelligence: Ethical, Legal and Technical Opportunities and Challenges", Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
15. Coeckelbergh, M. (2020) AI Ethics, Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/12549.001.0001
16. Constantinescu, M. & Kaptein, M. (2025) "Responsibility Gaps, LLMs & Organisations: Many Agents, Many Levels, and Many Interactions", Science and Engineering Ethics, 31(6), 36. https://doi.org/10.1007/s11948-025-00560-1
17. Crawford, K. (2021) Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, New Haven: Yale University Press. https://doi.org/10.12987/9780300252392
18. Danks, D. & London, A. J. (2017) "Algorithmic Bias in Autonomous Systems", Proceedings of the 26th International Joint Conference on Artificial Intelligence, s. 4691-4697. https://doi.org/10.24963/ijcai.2017/654
19. Dignum, V. (2017) "Responsible Artificial Intelligence: Designing AI for Human Values", ITU Journal: ICT Discoveries, 1(Special Issue 1), s. 1-8. http://handle.itu.int/11.1002/pub/8129fdfb-en
20. Dignum, V. (2019) Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way, Cham: Springer. https://doi.org/10.1007/978-3-030-30371-6
21. Dwivedi, Y. K.; Kshetri, N.; Hughes, L. et al. (2023) "'So What If ChatGPT Wrote It?' Multidisciplinary Perspectives on Opportunities, Challenges and Implications of Generative Conversational AI for Research, Practice and Policy", International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
22. Ebers, M. (2025) "Truly Risk-Based Regulation of Artificial Intelligence: How to Implement the EU's AI Act", European Journal of Risk Regulation, 16(2), s. 684-703. https://doi.org/10.1017/err.2024.78
23. Eisenhardt, K. M. (1989) "Agency Theory: An Assessment and Review", Academy of Management Review, 14(1), s. 57-74. https://doi.org/10.5465/amr.1989.4279003
24. Elish, M. C. (2019) "Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction", Engaging Science, Technology, and Society, 5, s. 40-60. https://doi.org/10.17351/ests2019.260
25. Ferrucci, D.; Levas, A.; Bagchi, S.; Gondek, D. & Mueller, E. T. (2013) "Watson: Beyond Jeopardy!", Artificial Intelligence, 199-200, s. 93-105. https://doi.org/10.1016/j.artint.2012.06.009
26. Floridi, L. (2014) The Fourth Revolution: How the Infosphere Is Reshaping Human Reality, Oxford: Oxford University Press. ISBN 978-0-19-960672-6. https://global.oup.com/academic/product/the-fourth-revolution-9780199606726
27. Floridi, L. & Cowls, J. (2019) "A Unified Framework of Five Principles for AI in Society", Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
28. Floridi, L. & Sanders, J. W. (2004) "On the Morality of Artificial Agents", Minds and Machines, 14(3), s. 349-379. https://doi.org/10.1023/B:MIND.0000035461.63578.9d
29. Gabriel, I.; Keeling, G.; Manzini, A. & Evans, J. (2025) "We Need a New Ethics for a World of AI Agents", Nature, 644(8075), s. 38-40. https://doi.org/10.1038/d41586-025-02454-5
30. 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, 51(5), s. 1-42. https://doi.org/10.1145/3236009
31. Jobin, A.; Ienca, M. & Vayena, E. (2019) "The Global Landscape of AI Ethics Guidelines", Nature Machine Intelligence, 1(9), s. 389-399. https://doi.org/10.1038/s42256-019-0088-2
32. Johnson, D. G. (2006) "Computer Systems: Moral Entities but Not Moral Agents", Ethics and Information Technology, 8(4), s. 195-204. https://doi.org/10.1007/s10676-006-9111-5
33. Kalluri, P. (2020) "Don't Ask If Artificial Intelligence Is Good or Fair, Ask How It Shifts Power", Nature, 583(7815), s. 169. https://doi.org/10.1038/d41586-020-02003-2
34. Kiener, M. (2025) "AI and Responsibility: No Gap, but Abundance", Journal of Applied Philosophy, 42(2), s. 357-374. https://doi.org/10.1111/japp.12765
35. Kirilenko, A. A.; Kyle, A. S.; Samadi, M. & Tuzun, T. (2017) "The Flash Crash: High-Frequency Trading in an Electronic Market", The Journal of Finance, 72(3), s. 967-998. https://doi.org/10.1111/jofi.12498
36. Martin, K. (2019) "Ethical Implications and Accountability of Algorithms", Journal of Business Ethics, 160(4), s. 835-850. https://doi.org/10.1007/s10551-018-3921-3
37. Matthias, A. (2004) "The Responsibility Gap: Ascribing Responsibility for the Actions of Learning Automata", Ethics and Information Technology, 6(3), s. 175-183. https://doi.org/10.1007/s10676-004-3422-1
38. Mittelstadt, B. D.; Allo, P.; Taddeo, M.; Wachter, S. & Floridi, L. (2016) "The Ethics of Algorithms: Mapping the Debate", Big Data & Society, 3(2), s. 1-21. https://doi.org/10.1177/2053951716679679
39. Moor, J. H. (2006) "The Nature, Importance, and Difficulty of Machine Ethics", IEEE Intelligent Systems, 21(4), s. 18-21. https://doi.org/10.1109/MIS.2006.80
40. Mukherjee, A. & Chang, H. H. (2025) "Agentic AI: Autonomy, Accountability, and the Algorithmic Society", arXiv preprint. https://doi.org/10.48550/arXiv.2502.00289
41. Nissenbaum, H. (1996) "Accountability in a Computerized Society", Science and Engineering Ethics, 2(1), s. 25-42. https://doi.org/10.1007/BF02639315
42. Noble, S. U. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism, New York: NYU Press. ISBN 978-1-4798-3724-3. https://nyupress.org/9781479837243/algorithms-of-oppression/
43. Novelli, C.; Casolari, F.; Rotolo, A.; Taddeo, M. & Floridi, L. (2024) "Taking AI Risks Seriously: A New Assessment Model for the AI Act", AI & Society, 39(5), s. 2493-2497. https://doi.org/10.1007/s00146-023-01723-z
44. Novelli, C.; Hacker, P.; Morley, J.; Trondal, J. & Floridi, L. (2025) "A Robust Governance for the AI Act: AI Office, AI Board, Scientific Panel, and National Authorities", European Journal of Risk Regulation, 16(2), s. 566-590. https://doi.org/10.1017/err.2024.57
45. NTSB (National Transportation Safety Board) (2019) Collision Between Vehicle Controlled by Developmental Automated Driving System and Pedestrian, Tempe, Arizona, March 18, 2018 (NTSB/HAR-19/03), Washington, DC: NTSB. https://www.ntsb.gov/investigations/accidentreports/reports/har1903.pdf
46. O'Neil, C. (2016) Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, New York: Crown. ISBN 978-0-553-41881-1. https://www.penguinrandomhouse.com/books/241363/weapons-of-math-destruction-by-cathy-oneil/
47. Pagallo, U. (2013) The Laws of Robots: Crimes, Contracts, and Torts (Law, Governance and Technology Series, Vol. 10), Dordrecht: Springer. https://doi.org/10.1007/978-94-007-6564-1
48. Papagiannidis, E.; Mikalef, P. & Conboy, K. (2025) "Responsible Artificial Intelligence Governance: A Review and Research Framework", The Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885
49. Parasuraman, R.; Sheridan, T. B. & Wickens, C. D. (2000) "A Model for Types and Levels of Human Interaction with Automation", IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 30(3), s. 286-297. https://doi.org/10.1109/3468.844354
50. Rahwan, I.; Cebrian, M.; Obradovich, N. et al. (2019) "Machine Behaviour", Nature, 568(7753), s. 477-486. https://doi.org/10.1038/s41586-019-1138-y
51. Raji, I. D.; Smart, A.; White, R. N. et al. (2020) "Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing", iç. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, New York: ACM. s. 33-44. https://doi.org/10.1145/3351095.3372873
52. Reisman, D.; Schultz, J.; Crawford, K. & Whittaker, M. (2018) Algorithmic Impact Assessments: A Practical Framework for Public Agency Accountability, New York: AI Now Institute. https://ainowinstitute.org/publications/algorithmic-impact-assessments-report-2
53. Ross, S. A. (1973) "The Economic Theory of Agency: The Principal's Problem", The American Economic Review, 63(2), s. 134-139. https://www.jstor.org/stable/1817064
54. Russell, S. (2019) Human Compatible: Artificial Intelligence and the Problem of Control, New York: Viking. ISBN 978-0-525-55861-3. https://www.penguinrandomhouse.com/books/566677/human-compatible-by-stuart-russell/
55. Russell, S. J. & Norvig, P. (2010) Artificial Intelligence: A Modern Approach (3rd ed.), Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4. https://aima.cs.berkeley.edu/
56. Scherer, M. U. (2016) "Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies", Harvard Journal of Law & Technology, 29(2), s. 353-400. https://jolt.law.harvard.edu/articles/pdf/v29/29HarvJLTech353.pdf
57. Shneiderman, B. (2020) "Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy", International Journal of Human-Computer Interaction, 36(6), s. 495-504. https://doi.org/10.1080/10447318.2020.1741118
58. Shrestha, Y. R.; Ben-Menahem, S. M. & von Krogh, G. (2019) "Organizational Decision-Making Structures in the Age of Artificial Intelligence", California Management Review, 61(4), s. 66-83. https://doi.org/10.1177/0008125619862257
59. Silver, D.; Huang, A.; Maddison, C. J. et al. (2016) "Mastering the Game of Go with Deep Neural Networks and Tree Search", Nature, 529(7587), s. 484-489. https://doi.org/10.1038/nature16961
60. Stahl, B. C. (2021) Artificial Intelligence for a Better Future: An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies, Cham: Springer. https://doi.org/10.1007/978-3-030-69978-9
61. Taddeo, M. & Floridi, L. (2018) "How AI Can Be a Force for Good", Science, 361(6404), s. 751-752. https://doi.org/10.1126/science.aat5991
62. Taeihagh, A. (2025) "Governance of Generative AI", Policy and Society, 44(1), s. 1-22. https://doi.org/10.1093/polsoc/puaf001
63. Vayena, E.; Blasimme, A. & Cohen, I. G. (2018) "Machine Learning in Medicine: Addressing Ethical Challenges", PLoS Medicine, 15(11), e1002689. https://doi.org/10.1371/journal.pmed.1002689
64. Véliz, C. (2020) Privacy Is Power: Why and How You Should Take Back Control of Your Data, London: Bantam Press. ISBN 978-1-78763-404-6.
65. Veluwenkamp, H. (2025) "What Responsibility Gaps Are and What They Should Be", Ethics and Information Technology, 27(1), 14. https://doi.org/10.1007/s10676-025-09823-8
66. Wallach, W. & Allen, C. (2009) Moral Machines: Teaching Robots Right from Wrong, Oxford: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195374049.001.0001
67. Wang, H. & Blok, V. (2025) "Why Putting Artificial Intelligence Ethics into Practice Is Not Enough: Towards a Multi-Level Framework", Big Data & Society, 12(2). https://doi.org/10.1177/20539517251340620
68. Winfield, A. F. & Jirotka, M. (2018) "Ethical Governance Is Essential to Building Trust in Robotics and Artificial Intelligence Systems", Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180085. https://doi.org/10.1098/rsta.2018.0085
69. Xi, Z.; Chen, W.; Guo, X. et al. (2023) "The Rise and Potential of Large Language Model Based Agents: A Survey", arXiv preprint. https://doi.org/10.48550/arXiv.2309.07864
70. Yampolskiy, R. V. (2013) "Artificial Intelligence Safety Engineering: Why Machine Ethics Is a Wrong Approach", iç. Philosophy and Theory of Artificial Intelligence (Studies in Applied Philosophy, Epistemology and Rational Ethics, Vol. 5), (Ed.) V. C. Müller, Berlin: Springer. s. 389-396. https://doi.org/10.1007/978-3-642-31674-6_29
71. Yu, M.; Meng, F.; Zhou, X. et al. (2025) "A Survey on Trustworthy LLM Agents: Threats and Countermeasures", arXiv preprint. https://doi.org/10.48550/arXiv.2503.09648
72. Zarsky, T. (2016) "The Trouble with Algorithmic Decisions: An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making", Science, Technology, & Human Values, 41(1), s. 118-132. https://doi.org/10.1177/0162243915605575
73. Zerilli, J.; Knott, A.; Maclaurin, J. & Gavaghan, C. (2019) "Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?", Philosophy & Technology, 32(4), s. 661-683. https://doi.org/10.1007/s13347-018-0330-6
74. Zuboff, S. (2019) The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, New York: PublicAffairs. ISBN 978-1-61039-569-4. https://www.hachettebookgroup.com/titles/shoshana-zuboff/the-age-of-surveillance-capitalism/9781610395694/