Cognitive Dissonance in Algorithmic Fact-Checking Systems

Abstract

This chapter explicates the psychological costs that arise when Cognitive-Epistemic Modeling is misaligned, misunderstood, or fails to resonate with human interpretive expectations. Algorithmically mediated fact-checking systems give rise to new forms of cognitive and emotional unease that extend the classical notion of the uncanny valley into epistemic and affective domains. Although the original concept described discomfort toward machines that imitate human appearance, contemporary AI systems now simulate reasoning, empathy, and moral discernment, unsettling the boundaries between human and computational modes of understanding. Guided by a theoretical framework that brings together algorithmic hermeneutics and affective infrastructures, this chapter identifies three interrelated dynamics: the algorithmic familiarity paradox, the cognitive transparency gap, and the ethical uncanny valley. These dynamics reveal how epistemic dissonance arises when algorithms reproduce gestures of human judgment without interpretive reciprocity or ethical transparency. The uneasy response emerges not from anthropomorphic design but from a deeper hermeneutic and moral misalignment between human and machine reasoning. The discussion contributes to debates on algorithmic epistemology by theorizing the conditions under which automated verification becomes epistemically unsettling and suggests design orientations that support interpretability and relational trust in human collaboration with AI.