Design Qualities, Trust, and Acceptance of Algorithmic Verification

Abstract

As AI-driven fact-checking becomes embedded in newsroom routines and platform governance, questions of legitimacy increasingly extend beyond technical performance. Public acceptance depends on how verification systems are designed, governed, and encountered in everyday information practices. In practice, algorithmic judgments are rarely evaluated on accuracy alone. Users also attend to whether decisions are intelligible, perceived as fair, accountable, and open to meaningful human engagement. Despite the rapid institutionalization of AI-assisted fact-checking, existing research offers limited guidance on how these governance-oriented design qualities work together to cultivate trust, or how trust, once established, shapes acceptance and epistemic effectiveness. This chapter develops a theory-driven Fact Checking Acceptance Model that treats trust not as a secondary attitude, but as the central mechanism linking system design to public acceptance. The chapter conceptualizes human oversight not as a technical failsafe, but as an epistemic calibration mechanism that reshapes how design cues are interpreted and how authority is distributed between automated systems and human judgment. By reframing algorithmic fact-checking as a sociotechnical and civic practice rather than a purely computational task, the chapter advances a mid-range theoretical framework for understanding acceptance in public-facing verification contexts and offers normative guidance for the design and governance of trustworthy AI-based fact-checking systems.