<![CDATA[The auditing profession is currently undergoing a significant transformation from a fully human-based approach to a human–AI collaborative model. Artificial intelligence (AI) is capable of processing large volumes of data, detecting anomalies, and identifying potential fraud with high speed and consistency, while human auditors continue to play a critical role in professional judgment, skepticism, and ethical decision-making. This systematic literature review examines the impact of this transition on audit quality (accuracy, efficiency, and fraud detection) and auditor independence (objectivity, professional skepticism, and external perception). A synthesis of 12 Scopus-indexed articles published between 2022 and 2025 indicates that human–AI collaboration enhances audit quality through comprehensive data analysis, reduction of manual errors, and more precise risk detection. Auditor independence remains preserved as long as active human oversight is maintained, although risks such as over-reliance on AI and algorithmic bias may arise under weak governance structures. Optimal strategies include continuous auditor training, robust AI governance frameworks, and manual verification procedures for critical outputs. The existing literature is predominantly focused on developed countries, highlighting the need for empirical research in Indonesia to ensure that implementation strategies align with local conditions, including infrastructure readiness and audit regulatory frameworks.]]>
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