Personalized AI Goal Recommendations and Worker Productivity in On-Demand Labor Markets
Keywords:
personalized goal recommendations, on-demand labor, algorithmic management, gig economy, AI fairness, worker autonomy, sociotechnical systemsAbstract
On-demand labor platforms increasingly rely on algorithmic systems to coordinate, evaluate, and motivate a distributed workforce. Among the emerging interventions, personalized goal recommendations—AI-generated suggestions for daily earnings targets, task volumes, or performance benchmarks—hold considerable promise for enhancing worker productivity while ostensibly supporting individual agency. Yet the introduction of such recommender infrastructures into gig economy ecosystems raises complex system-level questions concerning architectural design, governance, fairness, and long-term sustainability. This paper develops a sociotechnical analysis of personalized AI goal recommendation systems in on-demand labor markets, examining the structural trade-offs that arise when platforms seek to optimize productivity through tailored goal-setting nudges. We discuss how these systems extend existing algorithmic management regimes, the ways they interact with classical goal-setting theories, and the design choices required to balance personalization accuracy against worker autonomy and algorithmic transparency. A systems perspective reveals that deploying personalized goal recommendations without robust participatory governance and bias mitigation mechanisms risks deepening information asymmetries, diminishing worker autonomy, and amplifying existing disparities. Conversely, architectures that embed interpretability, worker feedback loops, and fairness constraints can transform goal recommendation engines into instruments that support both platform efficiency and worker well-being. The paper further addresses accountability frameworks, regulatory implications, and the infrastructural conditions necessary for sustainable deployment. By synthesizing perspectives from human-computer interaction, labor platform studies, and large-scale AI systems, we articulate a research and policy agenda for the responsible integration of personalized goal recommendation technologies in the future of digitally mediated work.
References
1. 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.
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. Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM.
4. Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3758–3784.
5. Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1603–1612). ACM.
6. Jarrahi, M. H., Sutherland, W., Nelson, S. B., & Sawyer, S. (2020). Platformic management, boundary resources for gig work, and worker autonomy. Computer Supported Cooperative Work (CSCW), 29, 153–189.
7. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717.
8. Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), 118–136.
9. Ho, C. J., Slivkins, A., Suri, S., & Vaughan, J. W. (2015). Incentivizing high quality crowdwork. In Proceedings of the 24th International Conference on World Wide Web (pp. 419–429). ACM.
10. Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender Systems: An Introduction. Cambridge University Press.
11. Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43.
12. Zuboff, S. (2015). Big other: Surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75–89.
13. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68). ACM.
14. Ajunwa, I. (2020). The ‘black box’ at work. Big Data & Society, 7(2), 2053951720966181.
15. Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104, 671–732.
16. 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.
17. Schor, J. B., Attwood-Charles, W., Cansoy, M., Ladegaard, I., & Wengronowitz, R. (2020). Dependence and precarity in the platform economy. Theory and Society, 49(5), 833–861.
18. De Stefano, V. (2016). The rise of the "just-in-time workforce": On-demand work, crowdwork and labour protection in the "gig-economy". Comparative Labor Law & Policy Journal, 37(3), 471–504.
19. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
20. Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56–62.
21. Berg, J., Furrer, M., Harmon, E., Rani, U., & Silberman, M. S. (2018). Digital Labour Platforms and the Future of Work: Towards Decent Work in the Online World. International Labour Organization.
22. Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on internet platforms. In Preconference on Data and Discrimination at the 65th Annual Conference of the International Communication Association.
23. Crawford, K., & Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. Boston College Law Review, 55, 93–128.
24. Frenken, K., & Schor, J. (2017). Putting the sharing economy into perspective. Environmental Innovation and Societal Transitions, 23, 3–10.
25. Scholz, T. (2016). Platform Cooperativism. Rosa Luxemburg Stiftung.
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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.
