AI-Augmented Earnings Targets and Worker Engagement in Last-Mile Delivery Platforms
Keywords:
AI-augmented earnings targets, last-mile delivery platforms, gig economy, algorithmic management, worker engagement, socio-technical systems, algorithmic fairness, platform governanceAbstract
The integration of artificial intelligence into the management of platform labor has introduced sophisticated mechanisms for shaping worker behavior, among which AI-augmented earnings targets represent a particularly consequential development. In last-mile delivery platforms, where a distributed workforce operates under conditions of high spatial and temporal variability, dynamically personalized income goals are increasingly deployed to sustain worker engagement and optimize system throughput. This paper provides a comprehensive system-level analysis of the architectures, incentive structures, and governance frameworks that underpin these AI-driven target-setting systems. We examine how earnings targets, when continuously adapted by learning algorithms that ingest real-time data on route demand, driver availability, weather conditions, and individual behavioral histories, function as both a coordination tool and a behavioral lever. The discussion foregrounds the structural trade-offs between platform efficiency and worker autonomy, the multi-objective optimization tensions among earnings maximization, delivery punctuality, and workload fairness, and the risks of algorithmic opacity inducing discriminatory or exploitative patterns. Through a cross-disciplinary lens that synthesizes insights from operations management, behavioral economics, human-computer interaction, and regulatory studies, we analyze the embedding of these systems within broader socio-technical infrastructures. The paper further assesses the implications for sustainability, fairness auditing, data governance, and the emerging regulatory environment surrounding platform work. We argue that without robust institutional oversight, transparent design principles, and meaningful worker participation in system parameterization, AI-augmented earnings targets risk reducing workers to passive executants of algorithmic directives, thereby undermining long-term engagement and systemic resilience. The analysis contributes foundational perspectives for researchers, system architects, and policymakers seeking to align AI-driven labor management with societal values.
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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.
