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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM42085

THE INVISIBLE WORKFORCE: EXPLORING THE IMPACT OF AI-DRIVEN EMPLOYEE MONITORING ON WORKPLACE TRUST AND EMPLOYEE RETENTION

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM42085

Volume:

9 |

3 |

Mar 2025

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM42085

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

JSK

J Sri Keerthana

Arts

PSG College of Arts & Science, Coimbatore

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Abstract

The growing adoption of artificial intelligence (AI) and digital tools has transformed how organizations assess, monitor, and manage their workforce. AI-based employee monitoring may involve automated activity tracking, monitoring of keystrokes and screens, productivity analysis, tracking of location, behavioral assessments, and performance evaluation supported by algorithms. While these systems can assist organizations in coordinating tasks, identifying operational issues, and safeguarding information, they also give rise to concerns about employee privacy, autonomy, fairness, and trust. This descriptive paper explores the essence of AI-driven employee monitoring and its potential impact on trust within the workplace and on employee retention. It analyzes the ways in which ongoing or unclear monitoring can influence employees' views on organizational justice, psychological safety, autonomy, and managerial support. The text also examines situations where monitoring might be deemed acceptable, especially when it is clear, reasonable, precise, and linked to legitimate job needs. This document employs a descriptive methodology derived from existing literature and theoretical discussions. It does not provide original survey results or assert empirical conclusions. The paper posits that employee monitoring ought to be regulated by explicit policies, effective communication, data minimization, human evaluation, and avenues for employees to challenge automated decisions. Thoughtful monitoring can aid in achieving organizational goals while ensuring respect for employee dignity and maintaining trust.

KEYWORDS

Artificial intelligence monitoring of employees surveillance in the workplace trust among employees retention of staff management through algorithms fairness in organizations and privacy for employees.

Cite This Article

J Sri Keerthana, "THE INVISIBLE WORKFORCE: EXPLORING THE IMPACT OF AI-DRIVEN EMPLOYEE MONITORING ON WORKPLACE TRUST AND EMPLOYEE RETENTION," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 3, pp. 1-7, Mar 2025. https://doi.org/10.55041/IJSREM42085

Full Article/Conclusion

AI-driven employee monitoring is becoming an important feature of digital workplaces. It can support operational coordination, safety, security and performance feedback, but it can also create concerns about privacy, autonomy, fairness and managerial control. These concerns matter because workplace trust is built through transparency, respect, consistency and accountability. When employees believe that monitoring is excessive, unexplained or unfair, their confidence in the organization may weaken and their willingness to remain may be affected. The impact of monitoring on retention is not automatic. It depends on the purpose and design of the system, the quality of communication, the fairness of its use and the wider employment environment. Organisations should therefore adopt a responsible and human-centered approach that combines data minimization, clear policies, employee participation, human oversight and effective review mechanisms. Technology should assist management, not replace fair judgement or undermine the dignity of employees. Sustainable adoption of AI in human resource management requires a balance between organizational efficiency and the trust of the people whose work is being monitored.

References

1.      Ajunwa, I., Crawford, K., & Schultz, J. (2017). Limitless worker surveillance. California Law Review, 105(3), 735–776. 2.      Ball, K. (2010). Workplace surveillance: An overview. Labor History, 51(1), 87–106. https://doi.org/10.1080/00236561003654776 3.      Bernstein, E. S. (2012). The transparency paradox: A role for privacy in organizational learning and operational control. Administrative Science Quarterly, 57(2), 181–216. https://doi.org/10.1177/0001839212453028 4.      De Stefano, V. (2019). ‘Negotiating the algorithm’: Automation, artificial intelligence, and labor protection. Comparative Labor Law & Policy Journal, 41(1), 15–46. 5.      Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174 6.      Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S. (2020). EPM 20/20: A review, framework, and research agenda for electronic performance monitoring. Journal of Management, 46(1), 100–126. https://doi.org/10.1177/0149206319869435 7.      International Labour Organization. (2021). World Employment and Social Outlook 2021: The role of digital labour platforms in transforming the world of work. International Labour Office.

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Publication Date

3/25/2025

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