Ensuring Human-in-the-loop Integrity: A Strategic HR Framework for AI-driven Compliance in Federal Agencies

Authors

  • Farzana Afroz MS in Human Resources, University of New Haven, Connecticut, USA , First Assistant Vice President, International Division, Social Islami Bank PLC, Bangladesh , Doctor of Business Administration and Management, University of Potomac, Washington, D.C., USA , Senior Researcher, Research & Innovation Division, Prime Bank PLC, Dhaka, Bangladesh
  • Tamanna Jahan MS in Human Resources, University of New Haven, Connecticut, USA , First Assistant Vice President, International Division, Social Islami Bank PLC, Bangladesh , Doctor of Business Administration and Management, University of Potomac, Washington, D.C., USA , Senior Researcher, Research & Innovation Division, Prime Bank PLC, Dhaka, Bangladesh
  • Ashim Sen Gupta MS in Human Resources, University of New Haven, Connecticut, USA , First Assistant Vice President, International Division, Social Islami Bank PLC, Bangladesh , Doctor of Business Administration and Management, University of Potomac, Washington, D.C., USA , Senior Researcher, Research & Innovation Division, Prime Bank PLC, Dhaka, Bangladesh
  • Rokhshana Parveen MS in Human Resources, University of New Haven, Connecticut, USA , First Assistant Vice President, International Division, Social Islami Bank PLC, Bangladesh , Doctor of Business Administration and Management, University of Potomac, Washington, D.C., USA , Senior Researcher, Research & Innovation Division, Prime Bank PLC, Dhaka, Bangladesh
  • Shuvashish Roy MS in Human Resources, University of New Haven, Connecticut, USA , First Assistant Vice President, International Division, Social Islami Bank PLC, Bangladesh , Doctor of Business Administration and Management, University of Potomac, Washington, D.C., USA , Senior Researcher, Research & Innovation Division, Prime Bank PLC, Dhaka, Bangladesh

Abstract

The rapid adoption of AI in public administration is altering decision-making processes and service delivery across government institutions. Federal agencies increasingly use AI systems for tasks such as fraud detection, benefit administration and public service management. Although these technologies are offering innovative efficiency, they also raise concerns regarding accountability, transparency and regulatory compliance. Several public organizations still face challenges in ensuring effective human oversight and governance of AI systems. So, how organizational governance mechanisms can strengthen AI-driven compliance effectiveness in government institutions have been examined in this work. The main purpose of the research is to analyze the effect of three organizational factors on AI compliance effectiveness: Human-in-the-Loop (HITL) oversight mechanisms, governance of AI infrastructure and strategic human resource capability for AI oversight. A quantitative research design using a structured survey instrument has been applied in the research. Data are analyzed using PLS-SEM to examine the relationships among the variables and to evaluate the validity and reliability of the measurement model. The findings confirm all three independent variables significantly influence AI compliance effectiveness. Human-in-the-Loop oversight has a positive strong effect, which indicates that structured human supervision improves accountability in algorithmic decision-making. AI governance infrastructure also demonstrates a significant connection with compliance effectiveness. Additionally, Strategic human resource capability strengthens compliance outcomes. The findings suggest that successful AI governance requires strong human oversight, clear institutional policies and skilled personnel who can manage algorithmic risks. Managers and policymakers should therefore develop formal AI governance frameworks, strengthen human supervision systems and invest in workforce training on AI ethics and compliance. Despite its contributions, the study has some limitations since cross-sectional design and reliance on survey responses were used.

References

Ada Lovelace Institute. (2021, August 24). Algorithmic accountability for the public sector. Ada Lovelace Institute. https://www.adalovelaceinstitute.org/project/algorithmic-accountability-public-sector/

Arif, M., Zhao, L., Zou, X., Li, K., Chen, Z., Ai, Y., & Tian, M. (2026). Ethics training competencies and leadership enable responsible AI in hospitality and tourism. iScience, 29(3), 115134. https://doi.org/10.1016/j.isci.2026.115134

Ayanwale, M. A., Omeh, C. B., Oyeniran, F. M., & Kanu, C. C. (2025). Ethical compliance and institutional policy support for artificial intelligence integration in African TVET education: A structural equation modeling approach. PLOS ONE, 20(12), e0335747. https://doi.org/10.1371/journal.pone.0335747

Bansal, A., Panchal, T., Jabeen, F., Mangla, S. K., & Singh, G. (2023). A study of human resource digital transformation (HRDT): A phenomenon of innovation capability led by digital and individual factors. Journal of Business Research, 157, 113611. https://doi.org/10.1016/j.jbusres.2022.113611

Eakin, H., Eriksen, S., Eikeland, P.-O., & Øyen, C. (2011). Public sector reform and governance for adaptation: Implications of new public management for adaptive capacity in Mexico and Norway. Environmental Management, 47(3), 338–351. https://doi.org/10.1007/s00267-010-9605-0

Engstrom, D. F., & Ho, D. E. (2019). Algorithmic accountability in the administrative state. Technology, Innovation, and Regulation, 37. https://administrativestate.gmu.edu/wp-content/uploads/2019/11/Engstrom-Ho-Algorithmic-Accountability-in-the-Administrative-State.pdf

European Union. (2024). Ethics guidelines for trustworthy AI. https://digital-strategy.ec.europa.eu/en/node/1950/printable/pdf

Fauzi, M. A. (2022). Partial least square structural equation modelling (PLS-SEM) in knowledge management studies: Knowledge sharing in virtual communities. Knowledge Management & E-Learning: An International Journal, 14(1), 103–124. https://doi.org/10.34105/j.kmel.2022.14.007

Fenwick, A., Molnar, G., & Frangos, P. (2024). Revisiting the role of HR in the age of AI: Bringing humans and machines closer together in the workplace. Frontiers in Artificial Intelligence, 6, Article 1272823. https://doi.org/10.3389/frai.2023.1272823

Gandía, J. A. G., Ancillo, A. de L., & del Val Núñez, M. T. (2025). Knowledge and artificial intelligence on employee behaviour advancing safe and respectful workplace. Journal of Innovation & Knowledge, 10(4), 100750. https://doi.org/10.1016/j.jik.2025.100750

Handoyo, S. (2024). Public governance and national environmental performance nexus: Evidence from cross-country studies. Heliyon, 10(23), e40637. https://doi.org/10.1016/j.heliyon.2024.e40637

Hao, X., Demir, E., & Eyers, D. (2025). Beyond human-in-the-loop: Sensemaking between artificial intelligence and human intelligence collaboration. Sustainable Futures, 10, 101152. https://doi.org/10.1016/j.sftr.2025.101152

Kelly, J. (2026, January 14). Research shows nearly 90% of U.S. government agencies use AI. Google Cloud Blog. https://cloud.google.com/blog/topics/public-sector/new-google-public-sector-research-shows-that-nearly-90-of-federal-agencies-are-already-using-ai

Kulal, A., Rahiman, H. U., Suvarna, H., Abhishek, N., & Dinesh, S. (2024). Enhancing public service delivery efficiency: Exploring the impact of AI. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100329. https://doi.org/10.1016/j.joitmc.2024.100329

Lawrence, C., Cui, I., & Ho, D. (2023). The bureaucratic challenge to AI governance: An empirical assessment of implementation at U.S. federal agencies. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (AIES '23), 606–652. https://doi.org/10.1145/3600211.3604701

Lazaros, K., Vrahatis, A. G., & Kotsiantis, S. (2026). Human-in-the-loop artificial intelligence: A systematic review of concepts, methods, and applications. Entropy, 28(4), 377. https://doi.org/10.3390/e28040377

Levinson-Waldman, R., & Reynolds, S. (2024, December 6). A start for AI transparency at DHS with room to grow. Brennan Center for Justice. https://www.brennancenter.org/our-work/analysis-opinion/start-ai-transparency-dhs-room-grow

Li, Y., Omar, M. K., Harithuddin, A. S. M., & Hassan, N. C. (2026). Aligning AI talent competencies with employer expectations: Graduate readiness in Guangzhou’s private sector. Sustainable Futures, 11, 101820. https://doi.org/10.1016/j.sftr.2026.101820

Monteiro, N., & Singh, V. (2025). The wheel of artificial intelligence governance. Sustainable Futures, 10, 101279. https://doi.org/10.1016/j.sftr.2025.101279

Nastase, C., Adomnitei, A., & Apetri, A. (2025). Strategic human resource management in the digital era: Technology, transformation, and sustainable advantage. Merits, 5(4), 23. https://doi.org/10.3390/merits5040023

NIST. (2021). AI risk management framework. National Institute of Standards and Technology. https://www.nist.gov/itl/ai-risk-management-framework

Oduro, S., Moss, E., & Metcalf, J. (2022). Obligations to assess: Recent trends in AI accountability regulations. Patterns, 3(11), 100608. https://doi.org/10.1016/j.patter.2022.100608

OECD. (2023). AI principles. OECD. https://www.oecd.org/en/topics/ai-principles.html

OECD. (2025a). Tax administration 2025: Comparative information on OECD and other advanced and emerging economies. OECD Publishing. https://doi.org/10.1787/cc015ce8-en

OECD. (2025b, September 18). Enablers, guardrails and engagement for unlocking trustworthy AI: Governing with artificial intelligence. https://www.oecd.org/en/publications/2025/06/governing-with-artificial-intelligence_398fa287/full-report/enablers-guardrails-and-engagement-for-unlocking-trustworthy-ai_2f817983.html

Olawade, D. B., Weerasinghe, K., Teke, J., Msiska, M., Boussios, S., & Hatzidimitriadou, E. (2025). Evaluating AI adoption in healthcare: Insights from the information governance professionals in the United Kingdom. International Journal of Medical Informatics, 199, 105909. https://doi.org/10.1016/j.ijmedinf.2025.105909

Panahi, S., Bazrafshani, A., & Mirzaie, A. (2023). Development and validation of a modified LibQUAL scale in health sciences libraries: Application of structural equation modeling. Journal of the Medical Library Association, 111(4), 792–801. https://doi.org/10.5195/jmla.2023.1348

Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885

Ringle, C. M., Wende, S., & Becker, J.-M. (2024). Discriminant validity assessment and heterotrait-monotrait ratio of correlations (HTMT)-SmartPLS 4 [Computer software]. SmartPLS. https://www.smartpls.com/

Romeo, E., & Lacko, J. (2026). Adoption and integration of AI in organizations: A systematic review of challenges and drivers towards future directions of research. Kybernetes, 55(3), 1286–1307. https://doi.org/10.1108/K-07-2024-2002

Sony, M. M. A. A. M., Amin, M. B., Ashraf, A., Islam, K. M. A., Debnath, N. C., & Debnath, G. C. (2025). Bias in AI-driven HRM systems: Investigating discrimination risks embedded in AI recruitment tools and HR analytics. Social Sciences & Humanities Open, 12, 102082. https://doi.org/10.1016/j.ssaho.2025.102082

Szucs, P. (2026). Digital civil servants: The integration of artificial intelligence into government workflows. Proceedings of the Central and Eastern European eDem and eGov Days 2025 (CEEeGov '25), 110–116. https://doi.org/10.1145/3773002.3773649

The White House. (2020, December 3). Executive order on promoting the use of trustworthy artificial intelligence in the federal government. https://trumpwhitehouse.archives.gov/presidential-actions/executive-order-promoting-use-trustworthy-artificial-intelligence-federal-government/

Toledo, R. R. (2026). Algorithmic accountability in public administration: A systematic review and conceptual framework for responsible AI governance. https://doi.org/10.31235/osf.io/j495x_v2

U.S. Government Accountability Office. (2026, March 26). Inside the IRS's use of artificial intelligence. U.S. Government Accountability Office. https://www.gao.gov/blog/inside-irs-use-artificial-intelligence

United Nations. (2014). Whole of government and collaborative governance. In United Nations e-government survey 2014 (pp. 75–93). United Nations. https://doi.org/10.18356/52cf7a88-en

U.S. Department of Justice. (2025, January 17). AI inventory. U.S. Department of Justice. https://www.justice.gov/ai/ai-inventory

Way, S. A., & Johnson, D. E. (2005). Theorizing about the impact of strategic human resource management. Human Resource Management Review, 15(1), 1–19. https://doi.org/10.1016/j.hrmr.2005.01.004

Xiang, X., & Wang, H. (2024). Overcoming one-sidedness in cadre personnel management: Developing a competency framework for China's central state-owned enterprise executives. Heliyon, 10(10), e31439. https://doi.org/10.1016/j.heliyon.2024.e31439

Yang, H. (2025). Governance: Perspectives of public administration, health administration, and primary care. Chinese General Practice Journal, 2(1), 100045. https://doi.org/10.1016/j.cgpj.2025.100045

Young, M. M., Himmelreich, J., Honcharov, D., & Soundarajan, S. (2022). Using artificial intelligence to identify administrative errors in unemployment insurance. Government Information Quarterly, 39(4), 101758. https://doi.org/10.1016/j.giq.2022.101758

Downloads

Published

2026-07-09

Issue

Vol. 3 No. 1 (2026): Don Bosco Institute of Technology Delhi Journal of Research (Jul-Dec)

Section

Research Article

How to Cite

Ensuring Human-in-the-loop Integrity: A Strategic HR Framework for AI-driven Compliance in Federal Agencies. (2026). Don Bosco Institute of Technology Delhi Journal of Research, 3(1), 1-16. https://www.acspublisher.com/journals/index.php/dbitdjr/article/view/24886