Artificial Intelligence, Inequality, and Economic Performance: An Empirical Investigation across Europe, USA, and China
References
- Aghion, P., Jones, B., & Jones, C. (2017). <em>Artificial intelligence and economic growth</em> (NBER Working Paper No. 23928). National Bureau of Economic Research.
- Baldwin, R. (2019). <em>The globotics upheaval: Globalisation, robotics and the future of work</em>. Weidenfeld & Nicolson.
- Fuentes Nettel, P., Hankins, E., Stirling, R., Cirri, G., Grau, G., Rahim, S., & Crampton, E. (2024). <em>Government AI Readiness Index 2024</em>. Oxford Insights. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://tic-guinee.net/wp-content/uploads/2025/02/2024-Government-AI-Readiness-Index-2.pdf">https://tic-guinee.net/wp-content/uploads/2025/02/2024-Government-AI-Readiness-Index-2.pdf</ext-link>
- Goldman Sachs Group, Inc. (2023). <em>2023 annual report</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.goldmansachs.com/investor-relations/financials/annual-reports/">https://www.goldmansachs.com/investor-relations/financials/annual-reports/</ext-link>
- Lane, P., Lafferty, R., Jackson, E., & Lafferty, C. (2023). Changing education with AI: Macro to micro. In <em>ICERI2023 Proceedings,</em> pp. 2910-2914. IATED.
- Latypova, V. (2023). Decision-making support in optimal multicriteria peer reviewer selection in scientific conference organization. In <em>2023 5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA)</em>, pp. 383-386. IEEE.
- Lestari, R., Windarwati, H. D., & Hidayah, R. (2024). Ai-driven decision-making applications in higher education. In <em>Using traditional design methods to enhance AI-driven decision making, </em>pp. 246-268. IGI Global Scientific Publishing.
- Limsiritong, T., Furutani, T., & Limsiritong, K. (2021). A challenge of macro-meso-micro analysis impacts on multiracial nationality decision making: Multiracial Thai-Japanese in Bangkok. <em>Inclusive Society and Sustainability Studies, 1</em>(2), 10–23.
- Liu, X., & Tang, J. (2021). Network representation learning: A macro and micro view. <em>AI Open, 2</em>, 43–64.
- Liu, Y., Zhao, L., Tian, J., Liu, J., & Wang, L. (2024). Artificial intelligence -based personalized clinical decision making for localized prostate cancer patients: Surgery versus radiotherapy.<em> The Oncologist</em>, <em>29</em>(12), e1692-e1700.
- Machlup, F. (2020). Micro- and macro-economics. <em>Economic Semantics</em>, 97–144. Routledge.
- Mashunin, Y. (2023). Digital transformation of optimal decision-making in economic and engineering systems based on the theory and methods of vector optimization. <em>Modern Intelligent Times, 1</em>(7).
- May, B., Milne, R., Shawyer, A., Meenaghan, A., & Ribbers, E. (2022). Identifying challenges to critical incident decision-making through a macro-, meso-, micro lens: A systematic synthesis and holistic narrative analysis. <em>Frontiers in psychology</em>, <em>14</em>, 1100274.
- Mayer, B., Fuchs, F., & Lingnau, V. (2023). Decision-making in the era of AI support— How decision environment and individual decision preferences affect advice taking in forecasts. <em>Journal of Neuroscience, Psychology, and Economics, 16</em>(1), 1–11.
- Mökander, J., & Axente, M. (2021). Ethics-based auditing of automated decision-making systems: Intervention points and policy implications. <em>AI & Society, 38</em>(1), 153–171.
- Morgan, D., Hashem, Y., Straub, V. J., & Bright, J. (2022). High-stakes team-based public sector decision making and AI oversight.
- Omerali, M., & Kaya, T. (2023). Energy efficiency optimization application in food production using IIoT-based machine learning. <em>Applied Innovation and Technology Management</em>, 185–204.
- Orlova, E. V. (2022). Design technology and AI-based decision-making model for digital twin engineering. <em>Future Internet, 14</em>(9), 248.
- Pirrone, A., Reina, A., & Gobet, F. (2021). Input-dependent noise can explain magnitude sensitivity in optimal value-based decision-making. <em>Judgment and Decision Making, 16</em>(5), 1221– 1233.
- Pitkäranta, T., & Pitkäranta, L. (2024). Bridging human and AI decision-making with LLMs: The RAGADA approach. In <em>Proceedings of the 26th International Conference on Enterprise Information Systems</em>, vol. 1, pp. 812-819.
DOI: https://doi.org/10.2478/picbe-2025-0254 | Journal eISSN: 2558-9652
Language: English
Page range: 3331 - 3346
Published on: Jul 24, 2025
Published by: The Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
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© 2025 Bouchra Al Mawla, published by The Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.