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Cited by 41Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Imbalanced Data Classification Techniques

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Mitigating Artificial Intelligence Bias in Financial Systems: A Comparative Analysis of Debiasing Techniques

Open access
Oguntibeju, Oluwatofunnmi O.·2024·Asian Journal of Research in Computer Science
DOI 10.9734/ajrcos/2024/v17i12536
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Abstract

Balancing fairness and predictive accuracy remains a key challenge in AI system development. This study investigates the origins of AI bias, how it happens in business processes, and the challenges it poses to ethical and transparent decision-making. Drawing on existing literature, the research explores the various types of biases—including cognitive, algorithmic, and representation biases—and their impact on AI systems in the BFSI sector. Furthermore, the study critically evaluates current debiasing techniques, such as pre-processing, fairness-aware models, and post-processing, highlighting their limitations in balancing fairness with predictive accuracy. This study aims to advance the development of more equitable AI systems in the BFSI sector by proposing the FAIR-BIAS Framework. This framework provides a structured approach to detecting, mitigating, and monitoring biases in AI models. Key recommendations include implementing equalized odds as a fairness metric to ensure balanced outcomes across demographic groups, applying adversarial debiasing techniques during model training to minimize discriminatory effects, and conducting regular data audits to ensure long-term fairness. The findings offer direct benefits for BFSI stakeholders. Businesses can enhance the reliability and ethical integrity of AI models by adopting fairness-aware risk assessments, which promote compliance and customer trust. Regulators can enforce accountability by mandating transparency measures, such as model explainability, and conducting periodic audits using fairness metrics like equalized odds. Policymakers can use the insights to create inclusive legislation which requires fairness testing and transparency in AI applications. Future research could explore the long-term effectiveness of debiasing techniques across different industries, such as healthcare or public policy, by conducting longitudinal studies to assess how evolving datasets and models influence fairness outcomes. It is critical for BFSI organizations to adopt these frameworks and techniques to foster a more inclusive and ethical future in financial services.”

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Research works printed but not linked: 17

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Metadata

Authors
Oguntibeju, O. O.
Affiliations
Bharti Airtel Limited (IN)
Corresponding author
Oguntibeju, Oluwatofunnmi O.
Venue
Asian Journal of Research in Computer Science
Publisher
Sciencedomain International
Published
2024-12-19
Volume
17
Issue
12
Pages
165-178
ISSN
2581-8260 (ISSN-L)
DOI
10.9734/ajrcos/2024/v17i12536
OpenAlex
W4405575183
Cited by
29
References
5
Citations by year
2026: 10 · 2025: 19
FWCI
12.02
Type
journal article
Language
en
Open access
Locations
  • Asian Journal of Research in Computer Science (publishedVersion)
  • Asian Journal of Research in Computer Science (publishedVersion, cc-by)
  • HAL (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 › Imbalanced Data Classification Techniques
Keywords
algorithmic bias, debiasing techniques, algorithmic fairness, financial services, equalized odds, adversarial debiasing, representation bias, cognitive biases, fairness metrics, model explainability, AI regulation, data auditing, fairness testing

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