Digital Technologies Research and Applications

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Algorithmic Bias in Automated Decision-Making: A Statistical Study with Legal and Regulatory Implications

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https://doi.org/10.54963/dtra.v5i2.2308
Dar, A. A., Jahan, S. A., Khan, M. S., Azad, I., Farooque, M. M. J., Sindhuja, S., & Abidha, A. K. (2026). Algorithmic Bias in Automated Decision-Making: A Statistical Study with Legal and Regulatory Implications. Digital Technologies Research and Applications, 5(2), 1–14. https://doi.org/10.54963/dtra.v5i2.2308
Volume 5 Issue 2: June 2026 (in progress)
Copyright (c) 2026 Amir Ahmad Dar, Shaik Afsar Jahan, Mohammad Shahfaraz Khan, Imran Azad, Murtaza M. Junaid Farooque, S. Sindhuja, A. K. Abidha
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Authors

  • Amir Ahmad Dar

    Department of Statistics, Lovely Professional University, Jalandhar 144411, India
  • Shaik Afsar Jahan

    Department of Statistics, Lovely Professional University, Jalandhar 144411, India
  • Mohammad Shahfaraz Khan

    College of Economics and Business Administration, University of Technology and Applied Sciences-Salalah, Salalah 211, Oman
  • Imran Azad

    College of Economics and Business Administration, University of Technology and Applied Sciences-Salalah, Salalah 211, Oman
  • Murtaza M. Junaid Farooque

    Department of MIS, College of Commerce and Business Administration, Dhofar University, Salalah 211, Oman
  • S. Sindhuja

    Department of Mathematics, SRM Institute of Science and Technology, Chennai 600089, India
  • A. K. Abidha

    Department Mathematics and Actuarial Science, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, India

Received: 19 January 2026; Revised: 23 February 2026; Accepted: 9 March 2026; Published: 9 April 2026

The use of algorithmic decision systems is being expanded to high-risk areas like credit, recruiting, and distributing government resources. Despite the fact that these systems are usually claimed to be objective and efficient, there have been apprehensions about the likelihood of structural inequalities being perpetuated by the systems. This paper examines the effect of a fairness-aware pre-processing technique called reweighing on the performance of a predictive system in a controlled simulation environment. Using a synthetically created credit approval dataset with structural disadvantage embedded, we compare the performance of a logistic regression classifier with and without reweighing. Fairness is measured using demographic parity disparity (DPD), disparate impact ratio (DIR), and equalized odds difference (EO), along with predictive accuracy. In a single test scenario (seed = 42), reweighing does not improve all fairness metrics uniformly. However, when analyzed for robustness across 50 independent random seeds, we find modest average reductions in demographic parity disparity and equalized odds difference for reweighing, with little change in predictive accuracy. Threshold sensitivity analysis also shows that fairness metrics are sensitive to decision thresholds. These results show that fairness-aware pre-processing can lead to systematic improvements in expectation, although trade-offs across fairness metrics and performance remain context-dependent.

Keywords:

Algorithmic Bias Fairness-Aware Machine Learning Demographic Parity Disparate Impact Equalized Odds AI Governance

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