AI-Assisted Goal Setting and Income Variability among Ride-Hailing and Delivery Workers
Keywords:
artificial intelligence, goal setting, gig economy, ride-hailing, delivery workers, income variability, algorithmic governance, platform labor, socio-technical systems, fairnessAbstract
The integration of artificial intelligence into the operational infrastructure of ride-hailing and delivery platforms has introduced novel mechanisms for worker self-regulation, most notably through AI-assisted goal setting systems. These systems, which leverage predictive algorithms and behavioral nudges, allow workers to set daily or weekly earnings targets, receive real-time progress updates, and adjust their labor supply in response to algorithmic suggestions. While such tools ostensibly empower workers by enhancing autonomy and financial planning, they simultaneously introduce new forms of algorithmic control and income variability. This paper examines the structural trade-offs embedded in AI-assisted goal setting within gig economy platforms, focusing on the architectural design of these systems, their governance implications, and their effects on worker welfare. We argue that goal setting algorithms, by optimizing for platform engagement metrics, often exacerbate income volatility rather than mitigate it, particularly under conditions of demand uncertainty and algorithmic opacity. Drawing on interdisciplinary frameworks from socio-technical systems theory, labor economics, and critical algorithm studies, we analyze how goal setting interfaces function as both cognitive aids and disciplinary mechanisms. We further explore the implications for platform governance, regulatory oversight, and the design of fairer algorithmic infrastructures. The paper concludes with recommendations for embedding robustness, transparency, and worker-centered fairness into the design of AI-assisted goal setting systems, emphasizing the need for multi-stakeholder governance models that account for the inherent asymmetries of platform labor markets.
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