Generative AI-Assisted Self-Regulation and Performance Improvement in Online Labor Platforms
Keywords:
generative AI, self-regulation, online labor platforms, algorithmic management, gig economy, socio-technical systems, performance improvement, platform governanceAbstract
Online labor platforms mediate an increasing share of global work, yet they surface persistent tensions between algorithmic management, worker autonomy, and performance sustainability. The recent emergence of generative artificial intelligence introduces a new class of assistive capabilities that can reshape how workers set goals, monitor progress, and adapt their task strategies in real time. This paper develops a system-level analysis of generative AI-assisted self-regulation for performance improvement in digital labor environments. We examine the architectural decomposition of a socio-technical feedback loop in which large language models serve as interactive reflective agents, helping workers calibrate effort, interpret performance signals, and negotiate platform constraints. The discussion foregrounds structural trade-offs between personalization and standardization, between real-time intervention and worker agency, and between local optimization and systemic robustness. Governance challenges are analyzed along dimensions of transparency, accountability, data sovereignty, and the risk of deepening power asymmetries between platform operators and workers. We consider infrastructure requirements for deploying such AI-mediated self-regulation at scale, addressing latency, model serving, continuous alignment, and the integration with legacy algorithmic management systems. Fairness implications are explored through the lens of differential access, bias amplification, and the potential for generative tools to homogenize work practices in ways that disadvantage minority strategies. The paper further connects these system dynamics to policy debates concerning worker classification, collective bargaining, and the design of platform architectures that foster sustainable digital careers. By synthesizing insights from human-computer interaction, organizational behavior, and distributed systems, this work contributes a multi-level framework for evaluating and designing generative AI interventions that seek to enhance, rather than undermine, worker self-determination and long-term performance in online labor platforms.
References
1. 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.
2. Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Good gig, bad gig: autonomy and algorithmic control in the global gig economy. Work, Employment and Society, 33(1), 56-75.
3. Möhlmann, M., Zalmanson, L., Henfridsson, O., & Gregory, R. W. (2021). Algorithmic management of work on online labor platforms: When matching meets control. MIS Quarterly, 45(4).
4. Jarrahi, M. H., & Sutherland, W. (2019). Algorithmic management and the future of work: Lessons from the platform economy. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
5. Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192.
6. Min, X., Chi, W., Hu, X., & Ye, Q. (2024). Set a goal for yourself? A model and field experiment with gig workers. Production and Operations Management, 33(1), 205-224.
7. Dell’Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., ... & Lakhani, K. R. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Technology & Operations Mgt. Unit Working Paper, (24-013).
8. Vallas, S., & Schor, J. B. (2020). What do platforms do? Understanding the gig economy. Annual Review of Sociology, 46, 273-294.
9. Rahman, H. A. (2021). The invisible cage: Workers’ reactivity to opaque algorithmic evaluations. Administrative Science Quarterly, 66(4), 945-988.
10. Cameron, L. D. (2022). “Making out” while driving: Relational and efficiency games in the gig economy. Organization Science, 33(1), 231-252.
11. Newlands, G. (2021). Algorithmic surveillance in the gig economy: The organization of work through Lefebvrian conceived space. Organization Studies, 42(5), 719-737.
12. Cram, W. A., Wiener, M., Tarafdar, M., & Junglas, I. (2022). Algorithmic controls and their implications for platform workers: A sociomaterial perspective. Information Systems Journal.
13. Boussioux, L., Lane, J. N., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2024). The crowdless crowd? AI's impact on contest-based problem solving. Organization Science.
14. Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from github copilot. arXiv preprint arXiv:2302.06590.
15. Benlian, A., Wiener, M., Cram, W. A., & Krasnova, H. (2022). Algorithmic management in action: The role of algorithmic transparency, control, and fairness. Business & Information Systems Engineering, 64(4), 425-437.
16. Schor, J. B., Attwood-Charles, W., Cansoy, M., Ladegaard, I., & Wengronowitz, R. (2020). Dependence and precarity in the platform economy. Theory and Society, 49, 833-861.
17. Veen, A., Barratt, T., & Goods, C. (2020). Platform-capital’s ‘app-etite’ for control: A labour process analysis of food-delivery work in Australia. Work, Employment and Society, 34(3), 388-406.
18. Sundararajan, A. (2016). The sharing economy: The end of employment and the rise of crowd-based capitalism. MIT Press.
19. Fuchs, C., & Sevignani, S. (2013). What is digital labour? What is digital work? What’s their difference? And why do these questions matter for understanding social media? tripleC: Communication, Capitalism & Critique, 11(2), 237-293.
20. Kässi, O., & Lehdonvirta, V. (2018). Online labour index: Measuring the online gig economy for policy and research. Technological Forecasting and Social Change, 137, 241-248.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
