Organizations now delegate consequential functions to algorithmic systems whose autonomous operations produce outcomes that exceed real-time human reconstruction, even when code and documentation are fully available. Existing trust theory was not built for this. Traditional mechanisms presuppose assessable intentions, human access points, and assignable probabilities; algorithmic environments jointly strain all three. We develop the concept of digital trust, the willingness to accept vulnerability through delegation to algorithmic systems whose operations are not fully graspable and whose behaviors may emerge beyond original design intent. The framework draws primarily on Luhmann’s systems theory, with Giddens and Coleman as complements, and turns on a paradox he identified: systems deployed to reduce complexity must themselves generate new complexity. That paradox is sharpened in algorithmic settings through temporal compression, emergent non-deducibility, and coupling at scale. We specify how trust forms across three analytically distinct levels—design, operations, and experience—and identify operative complexity as the residual condition that traditional mechanisms leave unresolved once algorithmic systems begin operating autonomously. Architectural decisions and operational experience shape trust through a dual temporal structure, working through different channels at different speeds. Trust transfers across algorithmic systems through recognition of shared enabling mechanisms rather than surface similarity alone. The argument is illustrated across four algorithmic settings: smart contracts, AI systems, IoT deployments, and platform ecosystems.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)