Algorithmic Fact-Verification

Abstract

As algorithmic systems increasingly mediate public discourse, the question of what constitutes truth and how it is constructed, stabilized, and contested demands renewed scrutiny. Automating Truth offers a comprehensive interdisciplinary inquiry into the evolving landscape of verification in the age of AI. Moving beyond binary distinctions of true and false, the book examines the sociotechnical infrastructures, epistemic assumptions, and institutional dynamics that underlie contemporary fact-checking systems. The book advances several key arguments. First, AI fact-checking does not simply verify information but reshapes the epistemic foundations of public knowledge by encoding norms of credibility and legitimacy into computational systems. Second, the automation of verification redistributes epistemic authority among humans, algorithms, and institutions, creating new forms of accountability and opacity. Third, cognitive and affective processes shape how users interpret algorithmic truth, revealing the need for epistemic alignment between human reasoning and machine logic. Fourth, democratic legitimacy in AI verification depends on designing systems that are transparent, contestable, and inclusive rather than purely efficient or predictive. Building on interdisciplinary research that bridges the humanities, social sciences, and computational design, the book develops frameworks such as Algorithmic Epistemology Theory and Cognitive-Epistemic Modeling to explain how truth is co-produced by human and computational actors. It concludes with a call for epistemic sustainability, envisioning truth-making as a civic and ethical practice rooted in transparency, inclusivity, and public accountability within the algorithmic public sphere.