Human-AI Collaborative Decision Frameworks for Fair Capacity Allocation among Competing Firms
Keywords:
capacity allocation, human-AI collaboration, algorithmic fairness, competing firms, governance, socio-technical systems, resource sharing, systems architectureAbstract
Capacity allocation among competing firms sits at the intersection of operations strategy, algorithmic fairness, and socio-technical governance, yet existing decision systems rarely integrate the complementary strengths of human judgment and artificial intelligence in ways that systematically safeguard equity. This paper develops a conceptual framework for human-AI collaborative decision architectures designed to achieve fair capacity allocation in multi-firm environments characterized by strategic interdependence, asymmetric information, and conflicting efficiency–fairness objectives. We argue that purely automated optimization, even when endowed with fairness constraints, cannot fully capture the normative pluralism, relational dynamics, and contextual sensitivities that define fairness across supply chains, cloud computing markets, and infrastructure networks. Conversely, unaided human committees suffer from cognitive biases, limited information-processing capacity, and inconsistency. By examining system-level design considerations—including modular accountability layers, participatory governance protocols, algorithmic auditing infrastructures, and adaptive feedback loops—we articulate structural trade-offs between computational tractability and deliberative legitimacy. The discussion draws on operations management, multi-agent resource allocation, fairness in machine learning, and institutional economics to illustrate how capacity-sharing platforms can embed human judgment at appropriate decision gates while leveraging algorithmic precision for pattern discovery, scenario simulation, and preference elicitation. Deployment considerations such as explainability, regulatory compliance, robustness under strategic manipulation, and long-term institutional sustainability are analyzed. The paper concludes by outlining a research agenda that bridges systems engineering and policy design, emphasizing the need for digitally mediated collective choice mechanisms that can adapt to evolving fairness norms in competitive ecosystems.
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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.
