Trust-Calibrated Platform Governance for AI-Powered Industrial Sharing Economies
Keywords:
trust calibration, platform governance, industrial sharing economy, artificial intelligence, capacity sharing, socio-technical systemsAbstract
The proliferation of artificial intelligence in industrial ecosystems is transforming traditional ownership models into fluid, multi-actor sharing economies for production capacity, logistics assets, and data-driven services. While these AI-powered platforms promise enormous efficiency gains, their viability hinges on the careful calibration of trust across heterogeneous participants who must expose proprietary operational data and strategic resources to algorithms and competitors. This paper develops the concept of trust-calibrated platform governance, a socio-technical framework that treats trust not as a monolithic condition but as a dynamic, multidimensional variable that must be engineered, monitored, and institutionally embedded. We examine the architectural underpinnings of industrial sharing platforms, focusing on the interplay between edge intelligence, federated learning, digital twins, and smart contract mechanisms that jointly shape trust perceptions. The analysis highlights structural trade-offs between transparency and privacy, automation and human oversight, as well as between algorithmic efficiency and equitable access. Drawing on systems thinking, we articulate a layered governance model that integrates technical trust enablers with contractual safeguards, reputation infrastructure, and multi-stakeholder regulatory oversight. We discuss challenges in robustness against adversarial manipulation, long-term sustainability of cooperative equilibria, and fairness in algorithmic allocation of shared industrial resources. Cross-domain comparisons with consumer sharing platforms illuminate the unique trust demands of capital-intensive, business-to-business contexts where reciprocity, capacity reliability, and liability chains are paramount. The paper concludes by outlining research directions for designing governance architectures that adaptively calibrate trust signals to the evolving risk landscapes of AI-mediated industrial collaboration.
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