Year: 2025
Type: article
Abstract: This article introduces ‘algorithmic truth’ to describe the epistemic shift as AI increasingly mediates public knowledge and legitimacy. 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 discussion fills this 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 discourse. 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 a more reflexive and participatory approach to the design and oversight of algorithmic verification.
Source: AI & Society
Author: Donghee Shin
Institution: Texas Tech University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
Funder: Texas Tech University
OpenAlex ID: W4413208012