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Ethical Implication of Artificial Intelligence (AI) Adoption in Financial Decision Making

Author

Listed:
  • Omoshola S. Owolabi
  • Prince C. Uche
  • Nathaniel T. Adeniken
  • Christopher Ihejirika
  • Riyad Bin Islam
  • Bishal Jung Thapa Chhetri

Abstract

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.

Suggested Citation

  • Omoshola S. Owolabi & Prince C. Uche & Nathaniel T. Adeniken & Christopher Ihejirika & Riyad Bin Islam & Bishal Jung Thapa Chhetri, 2024. "Ethical Implication of Artificial Intelligence (AI) Adoption in Financial Decision Making," Computer and Information Science, Canadian Center of Science and Education, vol. 17(1), pages 1-49, May.
    • Handle: RePEc:ibn:cisjnl:v:17:y:2024:i:1:p:49
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    References listed on IDEAS

    as
    1. Andrei Kirilenko & Albert S. Kyle & Mehrdad Samadi & Tugkan Tuzun, 2017. "The Flash Crash: High-Frequency Trading in an Electronic Market," Journal of Finance, American Finance Association, vol. 72(3), pages 967-998, June.
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    Cited by:

    1. Sukru Selim Calik & Andac Akyuz & Zeynep Hilal Kilimci & Kerem Colak, 2025. "Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100," Papers 2506.06345, arXiv.org.
    2. Ceray Aldemir & Tuğba Uçma Uysal, 2025. "Artificial Intelligence for Financial Accountability and Governance in the Public Sector: Strategic Opportunities and Challenges," Administrative Sciences, MDPI, vol. 15(2), pages 1-19, February.
    3. Rajesh Kumar & Amarjeet Kaur & Hamendra Kumar Dangi & Priyanka Kumari & Navneet Kumar, 2026. "Artificial Intelligence in Fire Safety: A Critical Perspective on Policy, Stakeholders and Emerging Technologies in India," FIIB Business Review, , vol. 15(1), pages 11-19, January.

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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