From Algorithmic Bias to Trustworthy Auditing: A Systematic Literature Review of AI Ethics, Accountability, Explainability, and Human Oversight
DOI:
https://doi.org/10.70142/jbs.v2i02.140Keywords:
Algorithmic Bias, AI Ethics, Audit Accountability, Explainable AI, Human Oversight, Smart value chainsAbstract
The rapid adoption of artificial intelligence (AI) in auditing creates significant opportunities for automation, analytical efficiency, and improved audit quality, while simultaneously introducing challenges related to algorithmic bias, ethics, accountability, explainability, and human oversight. This qualitative systematic literature review examines how these dimensions shape the development of trustworthy auditing. Using a systematic review approach, relevant literature was synthesized thematically across accounting, auditing, information systems, computer science, and AI ethics. The findings identify four interconnected dimensions of trustworthy AI enabled auditing: algorithmic bias and fairness, AI ethics and accountability, explainability and transparency, and human oversight and professional judgment. The review indicates that trustworthy auditing cannot be achieved through technological accuracy alone but requires integrated socio-technical governance mechanisms. The study contributes a conceptual perspective that connects ethical AI governance with professional auditing and identifies opportunities for future empirical research.
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