AI in Energy Sector HR Recruitment: Balancing Efficiency, Bias Reduction, and Human Touch

Afrin Sultana

doi:10.18034/apjee.v10i1.806

Authors

Afrin Sultana Lecturer, School of Business, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

DOI:

https://doi.org/10.18034/apjee.v10i1.806

Keywords:

Artificial Intelligence, Human Resource Management, Energy Sector, HR Recruitment, Bias Reduction, Human Touch

Abstract

Artificial intelligence (AI) is rapidly transforming human resource (HR) recruitment by improving hiring efficiency, reducing administrative workload, and supporting data-driven decision-making. In the energy sector, where organizations increasingly require highly skilled and specialized talent, AI-enabled recruitment systems offer significant opportunities to streamline candidate sourcing, screening, and selection. Despite these advantages, concerns remain regarding algorithmic bias, transparency, ethical decision-making, and the preservation of meaningful human interaction throughout the recruitment process. This study qualitatively examines the integration of AI into HR recruitment within the energy sector, emphasizing the balance between operational efficiency, bias reduction, and the irreplaceable value of human judgment. Drawing on recent literature, organizational perspectives, and selected evidence from digital transformation and AI implementation studies, the article explores how AI can complement rather than replace HR professionals in recruitment decisions. The findings suggest that AI substantially enhances recruitment speed, consistency, and predictive capability while supporting more objective candidate evaluation when appropriately governed. However, effective recruitment outcomes continue to depend on human empathy, contextual understanding, ethical oversight, and final managerial judgment. The study proposes a balanced recruitment framework in which AI functions as a strategic decision-support tool alongside HR expertise. The article contributes practical insights for HR managers and policymakers seeking to implement responsible AI-enabled recruitment practices that improve workforce quality while maintaining fairness, transparency, and human-centered organizational values in the evolving energy sector.

Downloads

Download data is not yet available.

References

Ahmed, A. A. A., & Khan, W. (2010). Project process management bridge between project and information technology. In Proceedings of the International Conference on Mechanical, Industrial and Energy Engineering (ICMIEE 2010) (p. 8). Faculty of Mechanical Engineering, KUET, Bangladesh.

Ahmed, A. A. A., Bynagari, N. B., Mustafa, M., Vishwakarma, S., & Azad, M. M. (2021). IoT and machine learning based low cost home automation and security system and methodology using cell phone (Canadian Patent No. 1188173). Canadian Patent Office.

Ahmed, A. A., Khan, W., & Hossain, M. S. (2011). Reporting practice of accounting disclosure on changes in listed companies of Bangladesh. ASA University Review, 5(1), 83–96.

Allam, A. R., Sarker, S. M. A.-E., & Nusaiba, M. T. (2022). Human-centered digital marketing: Shaping the future of customer engagement. Asian Journal of Humanity, Art and Literature, 9(2), 67–72.

Azad, M. R., Khan, W., & Ahmed, A. A. (2011). HR practices in banking sector on perceived employee performance: A case of Bangladesh. The Eastern University Journal, 3(3), 30–39.

Basu, S., Sinha, S., Bhattacharyya, S., & others. (2023). Artificial intelligence–HRM interactions and outcomes: A systematic review and causal configurational explanation. Human Resource Management Review, 33(1), 100893. https://doi.org/10.1016/j.hrmr.2022.100893

Chen, T.-C., Alazzawi, F. J. I., Guerrero, J. W. G., Chetthamrongchai, P., Dorofeev, A., Ismael, A. M., Ahmed, A. A. A., Akhmadeev, R., Latipah, A. J., & Abu Al-Rejal, H. M. E. (2022). Development of machine learning methods in hybrid energy storage systems in electric vehicles. Mathematical Problems in Engineering, 2022, Article 3693263, 1–8.

Forid, M. S., Hafez, M., & Khan, W. (2022). Student satisfaction and retention: Impact of service quality and digital transformation. Marketing and Management of Innovations, 4(1), 152–163.

Hunkenschroer, A. L., Kriebitz, A. (2022). Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring. AI and Ethics, 2(2), 199–213. https://doi.org/10.1007/s43681-022-00166-4

Jawad, M. A., Kawsher Rahman, K., & Sarker, S. M. A. (2022a). Artificial intelligence-driven public health strategies for sustainable business development. NEXG AI Review of America, 3(1), 48–66.

Jawad, M. A., Tasnim, H., & Nusaiba, M. T. (2022b). Artificial intelligence driven marketing analytics for sustainable business growth. Technology & Management Review, 7(1), 1–15.

Khan, W. (2022). Analyzing cloud reliability metrics and their influence on business continuity: The role of autonomous AI systems. Silicon Valley Tech Review, 1(1), 61–72.

Khan, W., & Ahmed, A. A. (2010). Recruitment, selection issues and challenges in public sector: Bangladesh case study. In Proceedings of the 4th Asian Business Research Conference. Bangladesh Institute of Administrative Management (BIAM) Foundation, Dhaka, Bangladesh.

Khan, W., & Fadziso, T. (2020). Ethical issues on utilization of AI, robotics and automation technologies. Asian Journal of Humanity, Art and Literature, 7(2), 79–90. https://doi.org/10.18034/ajhal.v7i2.521

Khan, W., Ahmed, A. A., Hossain, M. S., & Neogy, T. K. (2020). The interactive approach to working capital knowledge: Survey evidence. International Journal of Nonlinear Analysis and Applications, 11(Special Issue), 379–393. https://doi.org/10.22075/IJNAA.2020.4631

Khan, W., Hossin, M. E., & Akbor, M. J. (2015). Leather industry in Bangladesh: A systematic literature review. Asian Business Review, 5(3), 111–118.

Li, Z., Ahmed, A. A. A., Chupradit, S., Wisetsri, W., & Chupradit, P. W. (2021). Impact of psychological, mental, and socioeconomic factors on corruption in South Asia. Tobacco Regulatory Science, 7(6), 6708–6721.

Pessach, D., & Shmueli, E. (2020). Algorithmic fairness. ArXiv. https://arxiv.org/abs/2001.09784

Rahman, K., & Akhter, M. (2022). AI-driven decentralized diagnostics and point-of-care testing: Transforming healthcare marketing and future business through real-time clinical decision support. Technology & Management Review, 7(1), 16–24.

Strohmeier, S. (2020). Digital human resource management: A conceptual clarification. German Journal of Human Resource Management, 34(3), 345–365.

Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42.

Thompson, C. R., & Allam, A. R. (2022). The future of digital marketing: Balancing technology and human values. ABC Journal of Advanced Research, 11(2), 127–134.

van Esch, P., Black, J. S., & Ferolie, J. (2021). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215–222.

Votto, A., Valecha, R., Najafirad, P., & Rao, H. R. (2021). Artificial intelligence in tactical human resource management: A systematic literature review. International Journal of Information Management Data Insights, 1(2), 100047. https://doi.org/10.1016/j.jjimei.2021.100047

Vrontis, D., Christofi, M., Pereira, V., Tarba, S. Y., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33(6), 1237–1266. https://doi.org/10.1080/09585192.2020.1871398

Widjaja, G., Al-Thamir, M., Mehran, F., Yapanto, L. M., Fardeeva, I. N., Ahmed, A. A. A., Chupradit, S., Kukushkina, V. A., & Kuzichkin, O. R. (2022). Optimizing fluid flow cooling in gas transmission systems. Fluid Dynamics & Materials Processing, 18(4), 1099–1109.

Zhang, Y., Cao, X., & Wang, J. (2021). Artificial intelligence in talent acquisition: Opportunities, challenges, and future directions. Frontiers in Psychology, 12, Article 781379.

Zhilgildinova, M., Abibulayeva, A., Sultanova, N., Yedigenova, A., Seksenbayev, N., Ahmed,A. A. A., & Mengesha, R. W. (2022). Stimulating the professional and personal self-development of future teachers in the context of value-semantic orientation. Education Research International, 2022, Article 8789773.

Zirar, A., Ali, S. I., & Islam, N. (2020). Worker and workplace artificial intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747.