IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 5 2024
DOI: 10.56201/ijcsmt.v10.no5.2024.pg155.173

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The Role of Artificial Intelligence in Enhancing Fairness and Efficiency in Minimum Wage Adjustments

Umar Mohammed Pakra, Babangida Sadiq Muhammed, Mohammed, Usman

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Abstract

This study investigates the role of Artificial Intelligence (AI) in enhancing fairness and efficiency in minimum wage adjustments. With a focus on understanding participants' perspectives, data were collected from 36 individuals employed across various sectors. The analysis revealed that a significant majority (83%) of participants were employed full-time, primarily from the public sector (78%). Familiarity with AI was moderate, with 50% reporting familiarity, while knowledge of minimum wage adjustment processes varied. Participants expressed strong support for AI's potential to improve data analysis (46%) and reduce biases in wage policies (22%). However, challenges, including implementation costs (26.32%) and public trust in AI (27.66%), were also identified. The findings suggest a cautious optimism regarding AI's effectiveness in ensuring fair wage adjustments, with 70% of respondents acknowledging its potential. The study emphasizes the need for diverse representation, stakeholder collaboration, and ethical considerations in AI implementation. Recommendations are provided to facilitate AI integration into wage policy processes, ensuring comprehensive insights and equitable outcomes.

keywords:

Artificial Intelligence, Minimum Wage Adjustments, Algorithmic Bias, Data Privacy

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References:

Batchu, R. K. (2023). Artificial intelligence in credit risk assessment: Enhancing accuracy and
efficiency. ITAI, 7(7), 1-24.
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings
of the 2018 Conference on Fairness, Accountability, and Transparency, 149-159.
Brown, A. (2019). The role of AI in economic analysis. Journal of Economic Perspectives, 33(4),
67-85.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity
in a time of brilliant technologies. W. W. Norton & Company.
Dastin, J. (2018). Amazon scrapped a secret AI recruiting tool after it showed bias against women.
Reuters.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319-340.
De Stefano, V. (2019). Labor protection in the age of automation. Comparative Labor Law &
Policy Journal, 41(1).
Doe, J. (2021). AI in policy making: Case studies and applications. Policy and Technology Review,
12(3), 45-60.
Ernst, E., Merola, R., & Samaan, D. (2019). Economics of artificial intelligence: Implications for
the future of work. IZA Journal of Labor Policy, 9(4).
Garcia, M. (2022). Expert opinions on AI for minimum wage adjustments. Economic Innovations
Journal, 8(2), 99-113.
Kramer, A. D. I. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2),
1-22.
Lee, H., & Kim, S. (2018). Predictive analytics for economic forecasting. International Journal of
Data Science, 6(1), 23-40.
O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens
democracy. Crown Publishing Group.
Patel, R. (2020). Challenges in implementing AI-driven systems. Journal of Technology and
Society, 14(1), 77-92.
Sabil, S., Bangkara, B. M. A. S. A., Mogea, T., Niswan, E., & Timotius, E. (2023). Identification
of HRM improvement strategy using artificial intelligence in modern economic
development. International Journal of Professional Business Review, 8(6), 1-14.
https://doi.org/10.26668/ijpr.2023.e01835
Savage, L. J. (1954). The foundations of statistics. John Wiley & Sons.
Smith, J., & Jones, L. (2020). Machine learning in economic data analysis. Artificial Intelligence
Review, 44(3), 101-118.
Von Bertalanffy, L. (1968). General system theory: Foundations, development, applications.
George Braziller.
West, S. M. (2018). The ethics of AI and the future of work. Brookings Institution.
Zarsky, T. Z. (2016). Transparent predictions. University of Illinois Law Review, 2016(4), 1505-
1550.

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