TL;DR
This study treats an AI-generated evidence base as an object of appraisal and shows that its most consequential property is an absence.
The evaluation shows that IntelliAudit can support control interpretation, evidence-grounded reasoning, and audit-preparation workflows, while also revealing the importance of human oversight for calibrating sufficiency judgments and correcting overly permissive recommendations.
A graduated algorithmic reliance framework is proposed that ties the permissible depth of reliance on an AI system to the demonstrable explainability of that system, the materiality of the assertion it supports, and the auditor’s capacity to corroborate its output through independent means.
A systematic review of the literature on methodologies, frameworks, and techniques for auditing AI systems, focusing on legal and ethical considerations and compliance with regulations, reveals gaps in current auditing practices and highlights the importance of incorporating AI value chain stages and AI maturity levels...
The findings indicate that XAI is mainly applied to fraud detection, credit assessment, financial auditing, and decision-support processes, with a predominance of techniques such as SHAP and LIME.
AI systems are increasingly used to support consequential organizational decisions, heightening concerns about fairness, reliability, and accountability. Regulators have begun to respond through audit and disclosure mandates, yet credible assurance over AI systems remains underdeveloped and it is unclear who should p...
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