Ethical Implications of AI in Financial Services: Bias, Transparency, and Accountability

Authors

  • Puneet Chopra Panjab University, India Author

DOI:

https://doi.org/10.32628/CSEIT241051017

Keywords:

AI in Finance, Ethical Implications, Bias Mitigation, Transparency, Accountability

Abstract

This article examines the ethical implications of artificial intelligence (AI) in financial services, focusing on issues of bias, transparency, and accountability. As AI adoption in finance grows rapidly, with 85% of institutions now using AI, it brings both tremendous benefits and significant ethical challenges. The article explores how AI can perpetuate biases in lending and credit scoring, the "black box" problem of opaque AI decision-making, and the complexities of establishing accountability for autonomous AI systems. It analyzes strategies for mitigating these issues, including developing interpretable AI models, implementing robust governance structures, and creating industry-specific ethical guidelines. The discussion highlights the critical need for evolving regulatory frameworks and ethical standards to ensure responsible AI use while harnessing its potential to transform financial services.

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References

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Deloitte, "AI Leaders in Financial Services: Common traits of frontrunners in the artificial intelligence race," 2019. [Online]. Available: https://www2.deloitte.com/content/dam/insights/us/articles/4687_traits-of-ai-frontrunners/DI_AI-leaders-in-financial-services.pdf

European Banking Authority, "Report on Big Data and Advanced Analytics," 2020. [Online]. Available: https://www.eba.europa.eu/sites/default/documents/files/document_library/Final%20Report%20on%20Big%20Data%20and%20Advanced%20Analytics.pdf

Financial Stability Board, "Artificial intelligence and machine learning in financial services," 2017. [Online]. Available: https://www.fsb.org/wp-content/uploads/P011117.pdf

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Published

06-10-2024

Issue

Section

Research Articles

How to Cite

[1]
Puneet Chopra, “Ethical Implications of AI in Financial Services: Bias, Transparency, and Accountability”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 5, pp. 306–314, Oct. 2024, doi: 10.32628/CSEIT241051017.

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