Human–AI Joint Decision-Making in Flexible Labor Markets: A Large Language Model Framework for Goal Commitment and Performance Enhancement
Keywords:
human–AI collaboration, flexible labor markets, large language models, goal commitment, performance enhancement, socio-technical systems, algorithmic governance, fairnessAbstract
The rapid expansion of flexible labor markets, characterized by gig work, platform employment, and contingent contracting, has created new challenges for worker motivation, goal setting, and sustained performance. Traditional approaches to goal commitment rely on static, top-down management structures that are ill-suited to the fluid and decentralized nature of modern work. This paper proposes a framework for human–AI joint decision-making that leverages large language models to support dynamic goal formation, commitment tracking, and performance enhancement. The framework integrates LLM-based conversational agents with socio-technical system design principles to enable adaptive goal negotiation, real-time feedback, and personalized incentive alignment. We examine the architectural trade-offs involved in deploying such systems, including the balance between autonomy and control, the handling of goal conflict in multi-stakeholder environments, and the need for transparent governance mechanisms. The paper also addresses critical issues of fairness, algorithmic bias, and the sustainability of AI-mediated labor platforms. Through a cross-domain analysis of case studies from crowd work, on-demand delivery, and freelance consulting, we illustrate how LLM frameworks can improve worker engagement and productivity without undermining autonomy. We further discuss the infrastructural requirements for scalable deployment, the robustness of LLM-generated goal recommendations under uncertainty, and the policy implications for labor regulation and worker rights. The proposed framework contributes a systems-level perspective to the growing literature on human–AI collaboration, offering both theoretical foundations and practical design guidelines for next-generation labor market platforms.
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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.
