Epistemic Labor and Human–AI Collaboration in Fact-Checking

Abstract

In an era where misinformation spreads rapidly across digital platforms, human–AI collaboration has become a critical approach to fact-checking. While AI systems can accelerate verification, broaden coverage, and enable real-time monitoring, they cannot fully substitute for human interpretive judgment. Human evaluators remain indispensable for contextual reasoning, ethical oversight, and sustaining public trust. This chapter examines the practical benefits and systemic constraints of hybrid verification infrastructures. Drawing on empirical case studies, it argues that the value of AI lies not in replacing human evaluators but in reinforcing their judgment within sociotechnical workflows designed for transparency, responsiveness, and accountability. Effective collaboration depends on principled task allocation, user-centered design, and institutional frameworks that promote mutual intelligibility between human and machine agents. By embedding these principles into verification systems, human–AI collaboration can enhance epistemic resilience while safeguarding deliberative legitimacy in the governance of public knowledge. Such arrangements are essential for designing resilient, ethically grounded verification systems capable of responding to the complex dynamics of misinformation in a globalized information environment.