Explaining the Consequences of Artificial Intelligence for Auditor Independence, Professional Judgment, and Accountability in the Audit Process
Keywords:
Artificial intelligence, auditor independence, professional judgment, accountability, smart auditing, thematic analysis.Abstract
This study aimed to explain the consequences of artificial intelligence for auditor independence, professional judgment, and accountability in the audit process from the perspective of professional auditors in Tehran. This study adopted a qualitative research design based on thematic analysis. The participants were 22 independent auditors, audit managers, audit partners, and IT audit specialists working in Tehran, selected through purposive and snowball sampling. Data were collected through semi-structured interviews and continued until theoretical saturation was achieved. Saturation was reached at the nineteenth interview, followed by three additional interviews to confirm the stability of the emerging themes. The interviews were transcribed verbatim and analyzed using NVivo software through open, axial, and selective coding. Credibility was enhanced through member checking, peer review, and repeated comparison of codes and categories. The analysis revealed four main categories: redefining auditor independence under technological dependence, the dual role of AI in strengthening and weakening professional judgment, shifting accountability from the individual auditor to a human–algorithm chain, and the need for data governance and digital competence in auditing. Participants stated that AI can enhance audit quality by expanding data coverage, improving anomaly detection, and supporting evidence-based judgment. However, they also emphasized risks such as algorithmic opacity, overreliance on automated outputs, weakened professional skepticism, client-related technology dependence, and ambiguity regarding responsibility for AI-supported audit decisions. Artificial intelligence should not be viewed merely as a threat to auditing or as a substitute for auditors. Rather, it transforms the auditor’s role from performing repetitive procedures to critically supervising algorithmic outputs, interpreting audit evidence, and remaining accountable for final professional judgments. Preserving independence, judgment quality, and accountability in AI-enabled auditing requires ethical governance, explainable systems, professional training, documentation protocols, and a clear human-in-the-loop accountability framework.
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