Behavioral Heterogeneity in Gig Workers: An AI-Powered Goal Personalization Framework
Keywords:
gig economy, behavioral heterogeneity, goal personalization, AI-driven systems, algorithmic management, workforce sustainability, fairnessAbstract
The rapid expansion of digital labor platforms has created a vast and diverse workforce of gig workers whose behaviors, motivations, and work patterns exhibit profound heterogeneity. Traditional one-size-fits-all algorithmic management systems, primarily designed to optimize aggregate platform metrics, often fail to accommodate this diversity, leading to suboptimal worker engagement, high churn rates, and persistent fairness concerns. This paper introduces an AI-powered goal personalization framework that leverages behavioral heterogeneity as a central design principle rather than a noise factor to be eliminated. The framework integrates multi-modal data streams, causal machine learning, and constrained multi-armed bandit optimization to dynamically adapt performance targets, incentive structures, and task recommendations to individual worker profiles. We conduct a system-level analysis of the architectural requirements, highlighting trade-offs between personalization accuracy and computational overhead, the necessity of privacy-preserving infrastructure, and the challenges of real-time model updating in large-scale deployment. A substantial portion of the analysis is devoted to governance and policy implications, emphasizing the need for fairness-aware learning algorithms that prevent discriminatory targeting and the erosion of worker agency. We argue that sustainable gig economy ecosystems require a shift from extractive performance maximization toward worker-centric designs that align platform objectives with the long-term well-being and professional development of the workforce. The paper also examines robustness challenges including distributional shifts, cold-start problems for new workers, and adversarial behaviors that may arise in response to personalized incentives. Through this comprehensive framework, we contribute a system-oriented perspective on how artificial intelligence can be responsibly harnessed to foster a more inclusive, productive, and resilient gig economy.
References
1. 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.
2. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation. American Psychologist, 57(9), 705–717.
3. Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331–362.
4. Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010). A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th International Conference on World Wide Web (pp. 661–670). ACM.
5. Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3758–3784.
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. Chen, M. K., Chevalier, J. A., Rossi, P. E., & Oehlsen, E. (2019). The value of flexible work: Evidence from Uber drivers. Journal of Political Economy, 127(6), 2735–2794.
8. 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.
9. Kaur, H., Williams, A. C., Lasecki, W. S., & Teevan, J. (2022). Understanding the effects of personalized incentives on worker productivity in crowdsourcing. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), 1–26.
10. Cusumano, M. A., Gawer, A., & Yoffie, D. B. (2019). The business of platforms: Strategy in the age of digital competition, innovation, and power. Harper Business.
11. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
12. Athey, S., & Imbens, G. W. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353–7360.
13. Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J., & Wallach, H. (2018). A reductions approach to fair classification. In Proceedings of the 35th International Conference on Machine Learning (pp. 60–69). PMLR.
14. Mas, A., & Pallais, A. (2017). Valuing alternative work arrangements. American Economic Review, 107(12), 3722–3759.
15. Schwartz, A. L., Cox, C. R., & Williams, L. K. (2021). Using machine learning to personalize behavioral interventions. Behavioural Public Policy, 5(2), 238–260.
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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.
