Algorithmic Truth

Abstract

This chapter introduces the concept of algorithmic truth to describe the epistemological shift occurring as AI systems increasingly mediate public knowledge and determine the legitimacy of information. While prior work has examined misinformation detection and algorithmic bias, less attention has been paid to how AI systems themselves construct and reconfigure the epistemic conditions under which truth is produced and validated. This analysis fills the gap by offering a framework for understanding truth as a sociotechnical output of computational infrastructures. Algorithmic truth is neither neutral nor universal; it is embedded with normative assumptions, data-driven biases, and institutional logic that carry profound implications for epistemic authority, public trust, and democratic discourses. Positioned within broader debates on transparency, fairness, and accountability in the digital information ecosystem, the analysis concludes by outlining the sociopolitical risks of delegating epistemic functions to opaque computational systems and calls for design frameworks grounded in reflexivity (awareness of underlying assumptions) and participatory oversight involving diverse stakeholders.