Цифрова етика та алгоритмічна прозорість: виклики та методи забезпечення справедливості автоматизованих рішень у державному управлінні
Ключові слова:
цифрова етика, алгоритмічна прозорість, автоматизовані системи прийняття рішень, публічне управління, алгоритмічна справедливість, штучний інтелект, дискримінаційні та етичні ризики
Анотація
У статті досліджено ключові виклики цифрової етики та алгоритмічної прозорості в контексті впровадження автоматизованих систем прийняття рішень у державному управлінні. Виявлено чотири основні категорії етичних ризиків: дискримінаційні ризики, пов'язані з відтворенням та посиленням соціальних упереджень через алгоритмічні системи; ризики автономії, що виникають через зменшення людського контролю над важливими рішеннями; ризики підзвітності, обумовлені складністю визначення відповідальності в багаторівневих технічних системах; та ризики прозорості, що стосуються непрозорості алгоритмічних процесів для громадян та державних службовців.Систематизовано методи забезпечення алгоритмічної справедливості за трьома основними категоріями. Проаналізовано технічні методи, включаючи предобробку даних для усунення упереджень, справедливі алгоритми навчання з використанням багатоцільової оптимізації та адверсаріальних підходів, а також постобробку результатів через калібрування порогів прийняття рішень. Охарактеризовано процедурні гарантії, включаючи механізми участі громадян у розробці систем, створення доступних процедур оскарження алгоритмічних рішень та регулярний аудит алгоритмічних систем.Оцінено вплив алгоритмічної непрозорості на демократичні принципи та виявлено три основні виміри цієї проблеми: технічну непрозорість через складність алгоритмічних моделей, процедурну непрозорість через відсутність інформації про інтеграцію алгоритмів у державні процеси та організаційну непрозорість через складність розподілу відповідальності між різними акторами.Розроблено комплекс практичних рекомендацій для впровадження етичних алгоритмічних систем у державному управлінні.Завантаження
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REFERENCES
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Angwin, Julia, Jeff Larson, Surya Mattu, & Lauren Kirchner. (2016). Machine Bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
Anthes, G. (2015). Estonia: A Model for E-Government. Communications of the ACM 58, 6, 18-20. https://doi.org/10.1145/2749414.
Barocas, Solon, & Andrew D. Selbst. (2016). «Big Data’s Disparate Impact.» California Law Review 104(3), 671-732. https://doi.org/10.15779/Z38BG31.
Baumer, E., & Silberg, J. (2019). «Re -shaping AI to Serve Humans, Not Shareholders. In Towards a Human-AI Ecosystem, 1-15. Partnership on AI.
Berdo, R. S., Rasiun, V. L., & Velychko, V. A. (2023). Shtuchnyi intelekt ta ioho vplyv na etychni aspekty naukovykh doslidzhen v ukrainskykh zakladakh osvity. Akademichni Vizii, (22). https://doi.org/10.5281/zenodo.8174388
Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of Machine Learning Research 81, 149-59. http://proceedings.mlr.press/v81/binns18a.html.
Bullock, J. B. (2019). Artificial Intelligence, Discretion, and Bureaucracy. The American Review of Public Administration 49(7), 751-61. https://doi.org/10.1177/0275074019856123.
Burrell, J. (2016). How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms. Big Data & Society 3(1), 1-12. https://doi.org/10.1177/2053951715622512.
Chawla, Nitesh V., Kevin W. Bowyer, Lawrence O. Hall, & W. Philip Kegelmeyer. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16, 321-57. https://doi.org/10.1613/jair.953.
Chouldechova, A. (2017). Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data 5(2), 153-63. https://doi.org/10.1089/big.2016.0047.
Citron, D. K. (2007). Technological Due Process. Washington University Law Review, 85(6), 1249-313. https://openscholarship.wustl.edu/law_lawreview/vol85/iss6/2.
Citron, D. K., & Pasquale, F. (2014). The Scored Society: Due Process for Automated Predictions. Washington Law Review 89(1), 1-33. https://digitalcommons.law.uw.edu/wlr/vol89/iss1/2.
Coglianese, C., & Lehr, D. (2017). Regulating by Robot: Administrative Decision Making in the Machine-Learning Era. Georgetown Law Journal 105(5), 1147-223. https://georgetownlawjournal.org/articles/250/regulating-by-robot/pdf.
Council of Europe. (2018). Declaration by the Committee of Ministers on the Manipulative Capabilities of Algorithmic Processes. https://rm.coe.int/declaration-en/16808fa044
Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56–62. https://doi.org/10.1145/2844110
Durman, P., & Tokhtarova, I. (2023). Vyklyky tsyfrovoi transformatsii v ukrainskomu derzhavnomu upravlinni. Skhidnoievropeiskyi zhurnal derzhavnoi polityky, 9(2), 112–130.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (pp. 214–226). ACM. https://doi.org/10.1145/2090236.2090255
Edwards, H., & Storkey, A. (2016). Censoring representations with an adversary. (arXiv preprint arXiv:1511.05897). https://arxiv.org/abs/1511.05897
Engstrom, D. F., & Ho, D. E. (2020). Algorithmic accountability in the administrative state. Yale Journal on Regulation, 37(3), 800–854. https://digitalcommons.law.yale.edu/yjreg/vol37/iss3/2
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., et al. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
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
Green, B., & Chen, Y. (2019). Disparate interactions: An algorithm-in-the-loop analysis of fairness in risk assessments. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 90–99). ACM. https://doi.org/10.1145/3287560.3287563
Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems (pp. 3315-3323). https://proceedings.neurips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Kamiran, F., & Calders, T. (2012). Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems, 33(1), 1–33. https://doi.org/10.1007/s10115-011-0463-8
Karpenko, Yu. V. (2019). Etychni pryntsypy zastosuvannia shtuchnoho intelektu v publichnomu upravlinni. Visnyk Natsionalnoi akademii derzhavnoho upravlinnia pry Prezydentovi Ukrainy. Seriia: Derzhavne upravlinnia, (4), 93–97. http://nbuv.gov.ua/UJRN/vnaddy_2019_4_15
Kemper, J., & Kolkman, D. (2019). Transparent to whom? No algorithmic accountability without a critical audience. Information, Communication & Society, 22(14), 2081–2096. https://doi.org/10.1080/1369118X.2018.1477967
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. In 8th Innovations in Theoretical Computer Science Conference (pp. 43:1–43:23). https://doi.org/10.4230/LIPIcs.ITCS.2017.43
Kroll, J. A., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633-705. https://scholarship.law.upenn.edu/penn_law_review/vol165/iss3/3
Ministerstvo tsyfrovoi transformatsii Ukrainy. (2024). Rezultaty tsyfrovoi transformatsii v rehionakh Ukrainy za 2023 rik. https://www.kmu.gov.ua/news/rezultaty-tsyfrovoi-transformatsii-v-rehionakh-ukrainy-za-2023-rik
Mittelstadt, B. (2016). Auditing for transparency in content personalization systems. International Journal of Communication, 10, 4991–5002. https://ijoc.org/index.php/ijoc/article/view/6267
OECD. (2019). Recommendation of the Council on Artificial Intelligence. (OECD/LEGAL/0449). https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
Pedreshi, D., Ruggieri, S., & Turini, F. (2008). Discrimination-aware data mining. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 560–568). ACM. https://doi.org/10.1145/1401890.1401959
Peeters, R., & Widlak, A. (2018). The digital cage: Administrative exclusion through information architecture. Information, Communication & Society, 21(11), 1644–1659. https://doi.org/10.1080/1369118X.2018.1518468
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency(pp. 33–44). ACM. https://doi.org/10.1145/3351095.3372873
Raso, J., Hilligoss, H., Krishnamurthy, V., Bavitz, C., & Kim, L. (2018). Artificial intelligence & human judgment: Risk assessment in the criminal justice system. Berkman Klein Center for Internet & Society. https://dash.harvard.edu/handle/1/37342603
Reisman, D., Schultz, J., Crawford, K., & Whittaker, M. (2018). Algorithmic impact assessments: A practical framework for public agency accountability. AI Now Institute. https://ainowinstitute.org/aiareport2018.pdf
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). ACM. https://doi.org/10.1145/2939672.2939778
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68). ACM. https://doi.org/10.1145/3287560.3287598
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433
Veale, M., & Edwards, L. (2018). Clarity, surprises, and further questions in the Article 29 Working Party draft guidance on automated decision-making and profiling. Computer Law & Security Review, 34(2), 398–404. https://doi.org/10.1016/j.clsr.2017.10.002
Vyshnevskyi, O., & Kniaziev, S. (2024). Tsyfrova transformatsiia v Ukraini: dosiahnennia ta vyklyky. Ukrainskyi zhurnal derzhavnoho upravlinnia, 10(1), 88–104.
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005
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Опубліковано
2025-08-01
Як цитувати
Орлов , О. (2025). Цифрова етика та алгоритмічна прозорість: виклики та методи забезпечення справедливості автоматизованих рішень у державному управлінні. Аспекти публічного управління, 13(2), 51-60. https://doi.org/10.15421/152520
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