Explainable AI for Worker Motivation: Combining Goal-Setting Theory, SHAP Interpretability, and Reinforcement Learning in Online Labor Platforms
Keywords:
explainable AI, goal-setting theory, SHAP, reinforcement learning, online labour platforms, worker motivation, algorithmic management, fairness, governanceAbstract
Online labor platforms increasingly rely on algorithmic management to allocate tasks, set performance targets, and adjust incentives. While such systems optimise for platform-level efficiency, they often neglect the motivational dynamics that sustain long-term worker engagement. This paper proposes a novel framework that integrates goal-setting theory from organisational psychology with SHAP-based explainability and reinforcement learning to create a transparent, adaptive motivation system for platform workers. The architecture combines a reinforcement learning agent that personalises task difficulty and reward structures with a SHAP explanation engine that renders each algorithmic decision interpretable to the worker. We examine structural trade-offs between predictive accuracy and explanatory fidelity, between short-term optimisation and long-term motivation, and between platform profit and worker well-being. Governance implications are discussed, including the need for auditable policy constraints and fairness guarantees that prevent the system from exploiting behavioural vulnerabilities. By grounding machine-driven personalisation in established psychological theory and making its logic visible, the proposed design aims to foster trust, self-determination, and sustained productivity. The paper concludes with a discussion of deployment challenges, sustainability across heterogeneous worker populations, and the broader socio-technical infrastructure required for responsible implementation. This work contributes a systems-level perspective to the emerging intersection of explainable artificial intelligence, human motivation, and platform labour governance.
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