ISSN: 3108-0723 (Online)

International Journal of Technology, Health and Sustainability

IF: 2.64

as on 30st September, 2026.

International Journal of Technology, Health and Sustainability,

Responsible Artificial Intelligence in IT Auditing Through Evidence Validation, Human Oversight, and Regulatory Compliance

Okeoghene Elebe1 and Chukwudera Obumneke Anunagba2

1 IT Audit and SOC Consulting, Wipfli LLP, Atlanta, Georgia, USA.

2 Technology Analyst, US Consumer Bank Chief Data Office, Barclays, Whippany, New Jersey, USA.

Abstract

Artificial intelligence is entering audit workflows faster than many organizations are defining the evidence, accountability, and control requirements needed for defensible reliance. This study develops a responsible AI governance framework for IT auditing. A structured integrative review and design-science synthesis combines research on audit evidence, model risk, explainability, fairness, privacy, cybersecurity, and professional judgment with authoritative governance standards. The synthesis identifies six linked control domains: approved use and accountability, data provenance, model validation, explanation quality, protected human review, and monitored change. The framework requires each AI-assisted conclusion to remain traceable to an authorized purpose, a validated population, reproducible processing, documented reviewer judgment, and a retained disposition. It also separates model performance from audit quality. Accuracy, speed, or explanation output does not establish sufficient and appropriate evidence when data lineage, reviewer independence, or exception handling is weak. The proposed measures include provenance completeness, validation exceptions, subgroup error differences, override quality, unresolved findings, drift, remediation time, and recurrence. The study contributes a lifecycle model for bounded adoption and independent challenge. Its conceptual design does not demonstrate that AI improves audit outcomes. Field studies should compare AI-assisted and conventional procedures while measuring evidence quality, reviewer agreement, error patterns, cost, and residual risk.

Keywords: Responsible artificial intelligence, IT auditing, Algorithmic accountability, Human oversight, Model risk, Audit evidence

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