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Explainability and Transparency of AI in Auditing

Golubovskij, Vladislav (2025) Explainability and Transparency of AI in Auditing.

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Abstract:This thesis investigates the extent of explainability, and transparency of AI systems used in auditing. As AI technology increasingly supports audit procedures the need for transparent and explainable systems has become critical. Using a qualitative approach this study conducted semi-structured interviews with audit company representatives from companies such as Deloitte, EY, and Rabobank. The research applies a coding framework based on Explainable AI (XAI) theory to evaluate six criteria: clarity of explanation, user comprehension, trust, model design, transparency, data provenance, and bias detection. The findings of the research reveal that while AI systems offer valuable help in the process of audit tasks, their explainability is often limited to surface-level insights. Model design lacks depth for complex or high-risk engagements. Transparency is low, due to limited access to training data and model logic. This study concludes that AI systems are moderately transparent and explainable and are not technically sufficient enough to deliver a stand-alone value without human intervention. Auditors have to use AI as a supportive tool with close oversight, and for now, are not able to fully rely on only AI-generated outputs.
Item Type:Essay (Bachelor)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:International Business Administration BSc (50952)
Link to this item:https://purl.utwente.nl/essays/106877
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