<ul><li>Regulatory frameworks like the EU AI Act are starting to set guardrails for high-impact use cases of AI systems, emphasizing transparency and accountability.</li><li>Auditing AI systems is crucial for identifying risks, improving governance, and building confidence in how AI technology is developed and utilized.</li><li>Challenges in auditing AI systems include monitoring data pipelines, ensuring input data quality to avoid biased results, and assessing explainability, consistency, and fairness of AI-influenced decisions.</li><li>Frameworks like the Artificial Intelligence Audit Framework recommend specific areas for internal audit teams to focus on to ensure responsible AI usage, though challenges like evolving standards and lack of universal audit protocols persist.</li></ul>

The AI Audit Era: Navigating Risk in an Algorithmic World.

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