Spektrum Iran

Abstract This study examines the relationship between data bias, algorithmic ethics, and the social consequences of digital hermeneutics. As artificial intelligence increasingly influences interpretive domains—particularly religious and philosophical texts—the question of data neutrality and algorithmic objectivity has become a fundamental concern. Using an analytical-explanatory approach, the study demonstrate that training data, contrary to common assumptions, are not neutral. Instead, they embody cultural values and presuppositions that are reproduced within algorithmic processes. This reproduction can result in semantic simplification, the reduction of interpretive diversity, and even the distortion of sacred texts. Drawing on a hermeneutical perspective, the article emphasizes the need to distinguish between “human pre-understanding” and “machine data,” showing that the absence of awareness, critical reflexivity, and lived experience in algorithms prevents the attainment of authentic understanding. Moreover, the study indicates that the social implications of this condition extend beyond textual interpretation, posing risks to privacy, intensifying social inequalities, and undermining cultural diversity. Ultimately, the article argues that digital hermeneutics can be constructive only when the technical capacities of artificial intelligence are accompanied by ethical principles, religious oversight, and the preservation of interpretive traditions.