Digital Goal Architectures: An LLM-Driven Framework for Predicting Persistence and Earnings in Gig Economy Workforces
Keywords:
gig economy, large language models, predictive modeling, goal architectures, algorithmic management, worker persistence, earnings prediction, socio-technical systems, fairness, governanceAbstract
The rapid expansion of gig economy platforms has created urgent demands for systems that can sustain worker engagement and predict income trajectories under highly variable conditions. Traditional econometric models and rule-based management tools often fail to capture the dynamic interplay between worker autonomy, platform incentives, and evolving task environments. This paper introduces a conceptual framework termed Digital Goal Architectures, which integrates large language models (LLMs) into a predictive infrastructure designed to forecast persistence and earnings among gig workers. The framework positions LLMs as central reasoning engines that process heterogeneous data streams including worker goal statements, platform interaction logs, temporal effort patterns, and exogenous market signals. By leveraging the contextual understanding and generative capabilities of LLMs, the architecture moves beyond static goal-setting theory toward adaptive, personalized prediction that respects the situated nature of gig work. We examine the structural trade-offs inherent in deploying such systems, focusing on the tension between predictive accuracy and interpretability, the risks of algorithmic feedback loops that may exacerbate earnings inequality, and the governance challenges associated with real-time behavioral inference. The paper also addresses infrastructure requirements for scalable deployment, including latency constraints, data privacy safeguards, and the need for continuous model updating in non-stationary labor markets. Policy implications are discussed with an emphasis on transparency standards, worker consent mechanisms, and the design of fallback procedures that prevent harmful decisions. By situating LLM-driven predictive architectures within the broader socio-technical landscape of platform labor, this work provides a foundational perspective for researchers and practitioners aiming to build fair, robust, and sustainable systems for the future of work.
References
1. Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3-30.
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. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation. American Psychologist, 57(9), 705-717.
4. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Le, Q., Zhou, D., & Chi, E. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824-24837.
5. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
6. Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudík, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1-16.
7. Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press.
8. 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.
9. Chen, M. K., & Sheldon, M. (2016). Dynamic pricing in a labor market: Surge pricing and flexible work on the Uber platform. Working Paper.
10. Hall, J. V., & Krueger, A. B. (2018). An analysis of the labor market for Uber's driver-partners in the United States. ILR Review, 71(3), 705-732.
11. Kuhn, P. J., & Malchow-Møller, N. (2022). Online labor markets: A review. Journal of Economic Literature, 60(3), 895-945.
12. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning: Limitations and opportunities. MIT Press.
13. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59-68.
14. O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
15. Bucher, T. (2018). If... then: Algorithmic power and politics. Oxford University Press.
16. Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30.
17. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
18. Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., & Zhao, W. X. (2023). A survey on large language model based autonomous agents. arXiv preprint arXiv:2308.11432.
19. Möhlmann, M., & Zalmanson, L. (2017). Hands on the wheel: Navigating algorithmic management and Uber drivers. Proceedings of the International Conference on Information Systems.
20. 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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Global Financial Analytics Research Review
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.
