The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Ethical Implication of Artificial Intelligence (AI) Adoption in Financial Decision Making
Open accessAbstract
The integration of artificial intelligence (AI) into the financial sector has raised ethical concerns that need to be addressed. This paper analyzes the ethical implications of using AI in financial decision-making and emphasizes the importance of an ethical framework to ensure its fair and trustworthy deployment. The study explores various ethical considerations, including the need to address algorithmic bias, promote transparency and explainability in AI systems, and adhere to regulations that protect equity, accountability, and public trust. By synthesizing research and empirical evidence, the paper highlights the complex relationship between AI innovation and ethical integrity in finance. To tackle this issue, the paper proposes a comprehensive and actionable ethical framework that advocates for clear guidelines, governance structures, regular audits, and collaboration among stakeholders. This framework aims to maximize the potential of AI while minimizing negative impacts and unintended consequences. The study serves as a valuable resource for policymakers, industry professionals, researchers, and other stakeholders, facilitating informed discussions, evidence-based decision-making, and the development of best practices for responsible AI integration in the financial sector. The ultimate goal is to ensure fairness, transparency, and accountability while reaping the benefits of AI for both the financial sector and society.
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“It brings efficiency and personalization, but at the cost of fragmenting the collective experience and potentially narrowing personal horizons (Owolabi et al., 2024;Osasona et al., 2024;Oladoyinbo et al., 2024;Noranee & bin Othman, 2023).”
From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Owolabi et al., 2024
Citations
Cites
A Unified Framework of Five Principles for AI in Society
Floridi, L., & Cowls, J. (2019) · Harvard Data Science Review · doi:10.1162/99608f92.8cd550d1 · Cited by 1,631 · cited 2×
“These specialized bodies can serve as dedicated oversight entities, responsible for reviewing AI projects, assessing ethical implications, and providing guidance on aligning AI development and deployment with ethical principles (Floridi & Cowls, 2019).”
BackgroundThe Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1802.07228 · Cited by 849
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · Apollo - University of Cambridge Repository · doi:10.17863/cam.22520 · Cited by 849
The Flash Crash: High‐Frequency Trading in an Electronic Market
KIRILENKO, A., KYLE, A. S., SAMADI, M., & TUZUN, T. (2017) · The Journal of Finance · doi:10.1111/jofi.12498 · Cited by 795
Moore's Law versus Murphy's Law: Algorithmic Trading and Its Discontents
Kirilenko, A., & Lo, A. W. (2013) · The Journal of Economic Perspectives · doi:10.1257/jep.27.2.51 · Cited by 248
Privacy and Security Concerns in the Smart City
Fabrègue, B. F. G., & Bogoni, A. (2023) · Smart Cities · doi:10.3390/smartcities6010027 · Cited by 143
Olatoye, F. O., Awonuga, K. F., Mhlongo, N. Z., Ibeh, C. V., Elufioye, O. A., & Ndubuisi, N. L. (2024) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2024.11.1.0235 · Cited by 102
Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond
Schwendicke, F., & Krois, J. (2021) · Journal of Dental Research · doi:10.1177/0022034521998337 · Cited by 37
Ethical Considerations in AI-assisted Decision- Making for End-Of-Life Care in Healthcare.
Balasubramanian, S. (2023) · Power System Technology · doi:10.52783/pst.168 · Cited by 28
The Impact of Emerging Technologies on Sustainable Agriculture and Rural Development
Saikanth, D. R. K., Ragini, M., Tripathi, G., Rajesh, K., Giri, A., Pandey, S. K., & Verma, L. (2024) · International Journal of Environment and Climate Change · doi:10.9734/ijecc/2024/v14i13830 · Cited by 26
Generative artificial intelligence in finance
OECD (2023) · OECD artificial intelligence papers · doi:10.1787/ac7149cc-en · Cited by 17
Towards Responsible AI in Banking: Addressing Bias for Fair Decision-MakingPreprint
Castelnovo, A. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.08691 · Cited by 16
The Impact of Artificial Intelligence on Employment and Workforce Trends in the Post-Pandemic Era
Pavashe, A. S., Kadam, P. D., Zirange, V. B., & Katkar, R. D. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.56418 · Cited by 11
INTEGRATION OF ARTIFICIAL INTELLIGENCE INTO THE BLOCKCHAIN AND CRYPTOCURRENCY MARKET
Mandych, O., Ставерська, Т. О., & Maliy, O. (2023) · MODELING THE DEVELOPMENT OF THE ECONOMIC SYSTEMS · doi:10.31891/mdes/2023-10-8 · Cited by 8
Insider Threat Prevention in the US Banking System
Efijemue, O., Ejimofor, I., & Owolabi, O. S. (2023) · International Journal on Soft Computing · doi:10.5121/ijsc.2023.14302 · Cited by 7
Self-citationWine NFT cryptoassets: EU regulatory issues and market challenges
Jiménez, J. I. (2023) · BIO Web of Conferences · doi:10.1051/bioconf/20236803026 · Cited by 1
Cited by
Madanchian, M., & Taherdoost, H. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1619029 · Cited by 107
AI-Driven Chatbots in CRM: Economic and Managerial Implications across Industries
Khneyzer, C., Boustany, Z., & Dagher, J. (2024) · Administrative Sciences · doi:10.3390/admsci14080182 · Cited by 84
Aldemir, C., & Uysal, T. U. (2025) · Administrative Sciences · doi:10.3390/admsci15020058 · Cited by 78
Yang, Q., & Lee, Y. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8090105 · Cited by 57
AI-Driven risk assessment: Revolutionizing audit planning and execution
Onwubuariri, E. R., Adelakun, B. O., Olaiya, O. P., & Ziorklui, J. E. K. (2024) · Finance & Accounting Research Journal · doi:10.51594/farj.v6i6.1236 · Cited by 54
Metadata
- Authors
- Owolabi, O. S., Uche, P. C., Adeniken, N. T., Ihejirika, C., Islam, R. B., & Chhetri, B. J. T.
- Venue
- Computer and Information Science
- Published
- 2024-04-30
- Volume
- 17
- Issue
- 1
- Pages
- 49
- ISSN
- 1913-8989 (ISSN-L)
- 1913-8997
- OpenAlex
- W4396498564
- Cited by
- 56
- References
- 15
- Citations by year
- 2026: 17 · 2025: 30 · 2024: 9
- FWCI
- 23.32
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Computer and Information Science (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- AI ethics, financial decision-making, algorithmic bias, transparency and explainability, AI governance, ethical frameworks, accountability, public trust, financial regulation, responsible AI
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.