Risks of Decision-Making Organization and Implementation Based on Big Data Analytics and Artificial Intelligence

Risks of Decision-Making Organization and Implementation Based on Big Data Analytics and Artificial Intelligence

Authors

  • Ilya M. Kuznechenko

Keywords:

Big Data analytics, Big Data, public administration, artificial intelligence, decision-making, risk system.

Abstract

The search for additional tools that ensure the quality of organization and implementation of the decision-making by officials in the system of national and municipal government has become more and more relevant. Traditional approaches based on official statistics provide only a “snapshot of reality,” which is not always enough to understand the situation. Such achievements of technological progress, as big data analytics and artificial intelligence (hereinafter referred to as the technologies) make it possible to reconsider the scientific method of cognition that in practice offers additional tools to support the decision-making. To realize the potential of the technologies, it is necessary to overcome a number of problems, including those related to the organization and implementation of the decision-making process. The author considers the overall issue of uneven use of big data and artificial intelligence in the public sector, focusing on how the use of the technologies influences decision-making processes. There is a certain lag between the public sector and the private sector in the implementation of big data and artificial intelligence technologies and the wide opportunities of the government in the field of data generation compared to other economic entities. The research is based on the scientific literature analysis and case study methods. Based on the results of the study, the system of risks for organizing and implementing the decision-making process based on big data analytics and artificial intelligence is developed, which contributes to the formation of procedures for improving the quality of decisions in the system of public administration.

Author Biography

Ilya M. Kuznechenko

Head of the Project Management Department of the Department of Strategic Development and Corporate Policy
ORCID: 0000-0001-6832-6435
Ilya.kuznechenko@yandex.ru

Ministry of Industry and Trade of the Russian Federation, Moscow, Russian Federation

References

Амелин Р.В. Информационные системы как правовой инструмент воздействия на общественные отношения: анализ российской и мировой практики // Известия Саратовского университета. Новая серия. Серия: Экономика. Управление. Право. 2021. Т. 21. № 4. С. 445–452. DOI: 10.18500/1994-2540-2021-21-4-445-452

Бабкин Р.А. Опыт использования данных операторов сотовой связи в зарубежных экономико-географических исследованиях // Вестник Санкт-Петербургского университета. Науки о Земле. 2021. № 66(3). С. 416–439. DOI: 10.21638/spbu07.2021.301

Балацкий Е.В., Екимова Н.А. Прогнозирование настроений населения и идентификация «социальных пузырей» // Мониторинг общественного мнения. 2008. № 1(85). С. 62–71.

Балацкий Е.В., Юревич М.А. Измерение инфляционных ожиданий: традиционные и новаторские подходы // Вестник Санкт-Петербургского университета. Экономика. 2018. Т. 34. № 4. С. 534–552. DOI: 10.21638/spbu05.2018.403

Елизаров А.М., Паджев В.В., Хохлов Ю.Е. Система управления и механизмы финансирования работы с большими данными // Информационное общество. 2021. № 4–5. С. 53–65. DOI: 10.52605/16059921_2021_04_53

Исаков А., Латыпов Р., Репин А., Постолит Е., Евсеев А., Синельникова-Мурылева Е. Твердые цифры: открытые микроданные о потребительских ценах // Деньги и кредит. 2021. Т. 80. № 1. С. 104–119. DOI: 10.31477/rjmf.202101.104

Катин А.В., Хохлов Ю.Е. Мониторинг использования технологий работы с большими данными в системе государственного управления в России // Информационное общество. 2021. № 4–5. С. 150–165. DOI: 10.52605/16059921_2021_04_150

Кузнеченко И.М. Большие данные и искусственный интеллект в государственном управлении: анализ теории и выделение российских научных сообществ // Информационное общество. 2023. № 4. С. 127–146. DOI: 10.52605/16059921_2023_04_127

Кузнеченко И.М. Большие данные и искусственный интеллект, как факторы трансформации системы государственного управления // Экономическое развитие России. 2024. Т. 31. № 2. С. 113–128.

Морозов А.Н. Альтернативные источники статистической информации как основа принятия политических решений // Вопросы государственного и муниципального управления. 2018. № 2. С. 50–70.

Муминова С.Р. Технологии искусственного интеллекта как инструмент государственного управления в туризме // Среднерусский вестник общественных наук. 2022. Т. 17. № 5. С. 172–182. DOI: 10.22394/2071-2367-2022-17-5-172-182

Овчинский В.С., Ларина Е. Искусственный интеллект: Большие данные. Преступность. М.: Книжный мир, 2018.

Оксенойт Г.К. Цифровая повестка, большие данные и официальная статистика // Вопросы статистики. 2018. Т. 25. № 1. С. 3–16.

Умнова-Конюхова И.А., Ловцова Д.А. Государство и право в новой цифровой реальности. М.: РАН. ИНИОН, 2020.

Чаннов С.Е. Использование цифровых технологий в сфере публичного управления // Известия Саратовского университета. Новая серия. Серия: Экономика. Управление. Право. 2021. Т. 21. № 4. С. 419–428. DOI: 10.18500/1994-2540-2021-21-4-419-428

Ясыченко А.И. Большие данные в сфере электронных государственных и муниципальных услуг // Информационные технологии в управлении и экономике. 2022. № 3(28). С. 30–38.

Ananny M. Toward an Ethics of Algorithms: Convening, Observation, Probability, and Timeliness // Science, Technology, & Human Values. 2016. Vol. 41. Is. 1. P. 93–117. DOI: 10.1177/016224391560652

Barth T., Arnold E. Artificial Intelligence and Administrative Discretion: Implications for Public Administration // The American Review of Public Administration. 1999. Vol. 29. Is. 4. P. 332–351. DOI: 10.1177/0275074992206446

Boyd D., Crawford K. Critical Questions for Big Data. Information // Communication & Society. 2012. Vol. 15. Is. 5. P. 662–679. DOI: 10.1080/1369118X.2012.678878

Brauneis R., Goodman E.P. Algorithmic Transparency for the Smart City // Yale Journal of Law & Technology. 2018. Vol. 20. DOI: 10.2139/ssrn.3012499

Budhi G.S., Chiong R., Wang Z., Dhakal S. Using a Hybrid Content-based and Behavior-based Featuring Approach in a Parallel Environment to Detect Fake Reviews // Electronic Commerce Research and Applications. 2021. Vol. 47. DOI: 10.1016/j.elerap.2021.101048

Burrell J. How the Machine Thinks: Understanding Opacity in Machine Learning Algorithms // Big Data & Society. 2016. Vol. 3. Is. 1. DOI: 10.1177/2053951715622512

Cheung E., Chan A., Kajewski S. Reasons for Implementing Public Private Partnership Projects: Perspectives from Hong Kong, Australian and British Practitioners // Journal of Property Investment and Finance. 2009. Vol. 27. Is. 1. P. 81–95. DOI: 10.1108/14635780910926685

Chiao V. Fairness, Accountability and Transparency: Notes on Algorithmic Decision-making in Criminal Justice // International Journal of Law in Context. 2019. Vol. 15. Special Is. 2. P. 126–139. DOI: 10.1017/S1744552319000077

Coglianese C., Lehr D. Transparency and Algorithmic Governance // Administrative Law Review. 2019. Vol. 71. Is. 1. URL: https://scholarship.law.upenn.edu/cgi/viewcontent.cgi?article=3125&context=faculty_scholarship

Crawford K., Gray M., Miltner K. Critiquing Big Data: Politics, Ethics, Epistemology. Special Section Introduction // International Journal of Communication. 2014. Vol. 8. URL: https://ijoc.org/index.php/ijoc/article/view/2167/1164

Currie M. Data as Performance — Showcasing Cities through Open Data Maps // Big Data & Society. 2020. Vol. 7. Is. 1. DOI: 10.1177/2053951720907953

Danaher J. Freedom in an Age of Algocracy // Oxford Handbook of Philosophy of Technology / ed. by Sh. Vallor Oxford: Oxford University Press, 2020. Р. 250–272. DOI: 10.1093/oxfordhb/9780190851187.013.16

Desouza K., Jacob B. Big Data in the Public Sector: Lessons for Practitioners and Scholars // Administration & Society. 2014. Vol. 49. Is. 7. P. 1043–1064. DOI: 10.1177/0095399714555751

Dumbacher B., Hutchinson R. Enhancing the Foundation of Official Economic Statistics with Big Data // Estadística Española. 2018. Vol. 60. Núm. 197. P. 263–271.

Flores A., Bechtel K., Lowenkamp C. False Positives, False Negatives, And False Analyses: A Rejoinder to “Machine Bias: There’s Software Used Across the Country to Predict Future Criminals and It’s Biased Against Blacks” // Federal Probation Journal. 2016. Vol. 80. No. 2. URL: https://www.uscourts.gov/sites/default/files/80_2_6_0.pdf

Gal M. Algorithmic Challenges to Autonomous Choice // Michigan Journal of Law and Technology. 2018. Vol. 25. Is. 1. P. 59–104.

Giest S. Big Data for Policymaking: Fad or Fasttrack? // Policy Sciences. 2017. Vol. 50. P. 367–382. DOI: 10.1007/s11077-017-9293-1

Glaeser E., Hillis A., Kominers S., Luca M. Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy // American Economic Review. 2016. Vol. 106. Is. 5. P. 114–118. DOI: 10.1257/aer.p20161027

Glicksman R., Markell D.L., Monteleoni C. Technological Innovation, Data Analytics, and Environmental Enforcement // Ecology Law Quarterly. 2017. Vol. 44. Is. 1. P. 41–88.

Guenduez A., Mettler T., Schedler K. Technological Frames in Public Administration: What do Public Managers Think of Big Data? // Government Information Quarterly. 2020. Vol. 37. Is. 1. DOI: 10.1016/j.giq.2019.101406

Hackl P. Big Data: What Can Official Statistics Expect? // Statistical Journal of the IAOS. 2016. Vol. 32. Is. 1. P. 43–52. DOI: 10.3233/sji-160965

Höchtl J., Parycek P., Schöllhammer R. Big Data in the Policy Cycle: Policy Decision Making in the Digital Era // Journal of Organizational Computing and Electronic Commerce. 2016. Vol. 26. Is. 1–2. P. 147–169. DOI: 10.1080/10919392.2015.1125187

Holt D. Official Statistics, Public Policy and Public Trust // Journal of the Royal Statistical Society Series A. 2008. Vol. 171. Is. 2. P. 323–346. DOI: 10.1111/j.1467-985X.2007.00523.x

Howlett M. Policy Analytical Capacity and Evidence-Based Policy-Making: Lessons from Canada // Canadian Public Administration. 2009. Vol. 52. Is. 2. P. 153–175. DOI: 10.1111/j.1754-7121.2009.00070_1.x

Kaski S., Ailisto H., Suominen A. International AI Experts: Towards the Third Wave of Artificial Intelligence // Leading the Way into the Age of Artificial Intelligence: Final Report of Finland’s Artificial Intelligence Programme 2019. Helsinki: Ministry of Economic Affairs and Employment, 2019. P. 28–42.

Kitchin R. Big Data and Human Geography: Opportunities, Challenges and Risks // Dialogues in Human Geography. 2013. Vol. 3. Is. 3. P. 262–267. DOI: 10.1177/2043820613513388

Kitchin R. Big Data, New Epistemologies and Paradigm Shift // Big Data & Society. 2014. Vol. 1. Is. 1. DOI: 10.1177/2053951714528481

Kitchin R., Stehle S. Can Smart City Data Be Used to Create New Official Statistics? // Journal of Official Statistics. 2021. Vol. 37. Is. 1. P. 121–147. DOI: 10.2478/jos-2021-000

Klievink B., Romijn B.-J., Cunningham S., de Bruijn H. Big Data in the Public Sector: Uncertainties and Readiness // Information Systems Frontiers. 2017. Vol. 19. P. 267–283. DOI: 10.1007/s10796-016-9686-2

Kogan M. The Impact of Research on Policy // Speaking Truth to Power: Research and Policy on Lifelong Learning / ed. by F. Coffield. Bristol, UK: Policy Press, 1999. P. 11–18.

Kroll J.A., Huey J., Barocas S., Felten E.W., Reidenberg J.R., Robinson D.G., Yu H. Accountable Algorithms // The University of Pennsylvania Law Review. 2017. Vol. 165. URL: https://scholarship.law.upenn.edu/penn_law_review/vol165/iss3/3/

Liu H.W., Lin C.F., Chen Y.J. Beyond State v. Loomis: Artificial Intelligence, Government Algorithmization, and Accountability // International Journal of Law and Information Technology. 2019. Vol. 27. Is. 2. P. 122–141. DOI: 10.1093/ijlit/eaz001

Maciejewski M. To Do More, Better, Faster and More Cheaply: Using Big Data in Public Administration // International Review of Administrative Sciences. 2017. Vol. 83. Is. 1 suppl. P. 120–135. DOI: 10.1177/0020852316640058

Marchi G., Lucertini G., Tsoukiàs A. From Evidence-Based Policy-Making to Policy Analytics // Annals of Operations Research. 2016. Vol. 236. Is. 1. P. 15–38. DOI: 10.1007/s10479-014-1578-6

Marmot M. Evidence Based Policy or Policy Based Evidence? // BMJ. 2004. Vol. 328. No. 7445. P. 906–907. DOI: 10.1136/bmj.328.7445.906

Mayer-Schonberger V., Cukier K. Big Data: A Revolution that Will Change How We Live, Work and Think. Boston, New York: An Eamon Dolan Book; Houghton Mifflin Harcourt, 2013.

McAfee A., Brynjolfsson E. Big Data: The Management Revolution // Harward Business Review. 2012. Vol. 90. Is. 10. P. 60–66.

Medda F., Carbonaro G., Davis S. Public Private Partnerships in Transportation: Some Insights from the European Experience // IATSS Research. 2013. Vol. 36. Is. 2. P. 83–87. DOI: 10.1016/j.iatssr.2012.11.002

Mikhaylov S.J., Esteve M., Campion A. Artificial Intelligence for the Public Sector: Opportunities and Challenges of Cross-Sector Collaboration // Philosophical Transactions of the Royal Society A. 2018. Vol. 376. Is. 2128. DOI: 10.1098/rsta.2017.0357

Miller H. The Data Avalanche is Here. Shouldn’t We Be Digging? // Journal of Regional Science. 2010. Vol. 50. Is. 1. P. 181–201. DOI: 10.1111/j.1467-9787.2009.00641.x

Mittelstadt B., Allo P., Taddeo M., Wachter S., Floridi L. The Ethics of Algorithms: Mapping the Debate // Big Data & Society. 2016. Vol. 3. Is. 2. DOI: 10.1177/2053951716679679

Nelkin D. The Political Impact of Technical Expertise // Social Studies of Science. 1975. Vol. 5. Is. 1. P. 35–54.

O’Neil C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Washington, D.C.: Crown Books, 2016.

Pasquale F. A Rule of Persons, Not Machines: The Limits of Legal Automation // George Washington Law Review. 2019. Vol. 87. Is. 1. URL: https://www.gwlr.org/wp-content/uploads/2019/01/87-Geo.-Wash.-L.-Rev.-1.pdf

Poel M., Meyer E.T., Schroeder R. Big Data for Policymaking: Great Expectations, but with Limited Progress? // Policy & Internet. 2018. Vol. 10. Is. 3. P. 347–367. DOI: 10.1002/poi3.176

Prensky M.H. Sapiens Digital: From Digital Immigrants and Digital Natives to Digital Wisdom // Innovate: Journal of Online Education. 2009. Vol. 5. Is. 3. URL: https://www.learntechlib.org/p/104264/

Rosenstock L., Lee L.J. Attacks on Science: The Risks to Evidence-based policy // American Journal of Public Health. 2002. Vol. 92. Is. 1. P. 14–18. DOI: 10.2105/ajph.92.1.14

Sabatier P. Toward Better Theories of the Policy Process // PS: Political Science & Politics. 1991. Vol. 24. Is. 2. P. 147–156.

Siegel E. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Hoboken: Wiley, 2013.

Stahl B. Artificial Intelligence for a Better Future: An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies. Berlin: Springer Nature, 2021.

Tiguint B. AI and Big Data Strategy in the Public Sector: Toward the State 4.0 // Искусственный интеллект и тренды цифровизации: техногенный прорыв как вызов праву. М.: Российский университет транспорта, 2021. С. 67–81.

Van der Voort H.G., Klievink A.J., Arnaboldi M., Meijer A.J. Rationality and Politics of Algorithms. Will the Promise of Big Data Survive the Dynamics of Public Decision Making? // Government Information Quarterly. 2019. Vol. 36. Is. 1. P. 27‒38. DOI: 10.1016/j.giq.2018.10.011

Vydra S., Klievink B. Techno-Optimism and Policy-Pessimism in the Public Sector Big Data Debate // Government Information Quarterly. 2019. Vol. 36. Is. 4. DOI: 10.1016/j.giq.2019.05.010

Walker R. Welfare Policy: Tendering for Evidence // What works? Evidence-Based Policy and Practice in Public Services / ed. by S. Nutley, P. Smith, H. Davies. Bristol: Policy Press, 2000. P. 141–166. DOI: 10.1332/policypress/9781861341914.003.0007

Wexler R. Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System // Stanford Law Review. 2018. Vol. 70. URL: https://review.law.stanford.edu/wp-content/uploads/sites/3/2018/06/70-Stan.-L.-Rev.-1343.pdf

Zarsky T. Transparent Predictions // University of Illinois Law Review. 2013. Vol. 2013. Is. 4. P. 1503–1569.

Downloads

Published

2024-06-30

How to Cite

Risks of Decision-Making Organization and Implementation Based on Big Data Analytics and Artificial Intelligence. (2024). Public Administration. E-Journal (Russia), 104, 162-180. https://doi.org/10.24412/ym8t4a89

Issue

Section

Scientific articles

Categories

How to Cite

Risks of Decision-Making Organization and Implementation Based on Big Data Analytics and Artificial Intelligence. (2024). Public Administration. E-Journal (Russia), 104, 162-180. https://doi.org/10.24412/ym8t4a89

Most read articles by the same author(s)

<< < 28 29 30 31 32 33 34 35 36 > >> 

Similar Articles

1-10 of 312

You may also start an advanced similarity search for this article.

Loading...