From Narrator Criticism to Digital Identity Criticism: A Reconstruction of the Principle of al-Jarḥ wa al-Ta’dīl

Abstract

Digital information verification has thus far been confined to content assessment, neglecting the authenticity of source identity, precisely the element currently being fabricated by deepfake technology. This gap underlies the present study's attempt to reconstruct the principle of al-jarḥ wa al-ta'dīl into a digital identity criticism model. To address this gap, a qualitative method using a grounded theory approach is employed to develop a conceptual framework for the transformation of al-jarḥ wa al-ta'dīl from a method of narrator credibility criticism into a framework for digital identity criticism in verifying deepfake hoaxes. Data were collected through documentation techniques, namely inventorying, closely reading, recording, classifying, and comparing documents according to themes relevant to the research focus, and were subsequently analyzed using the interactive model of Miles et al., encompassing data condensation, data display, and conclusion drawing. Findings indicate that the reconstruction of al-jarḥ wa al-ta'dīl broadens the scope of hadith criticism, extending it from evaluating narrator credibility to criticizing source authenticity and credibility within an artificial-intelligence-based digital information ecosystem. 'Adālah and ḍabṭ are shown to possess a more fundamental epistemological function, extending beyond their conventional role as classical criteria for the acceptance of narrators (rāwī). This function further signals a fundamental shift in the structure of informational authority, which in turn gives rise to a model of digital identity criticism grounded in the principles of al-jarḥ wa al-ta'dīl. As its principal contribution, digital identity criticism is formulated as a conceptual and methodological development within hadith studies, repositioning the function of al-jarḥ wa al-ta'dīl from criticizing narrator credibility toward an epistemological framework for evaluating synthetic sources in the deepfake era.