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Data interpretation and model explainability - many auditors rely on tools but cannot yet articulate how results are produced
Not necessarily. Understanding logic, model behavior, and data relationships is more important than coding proficiency
Begin with data literacy programs, followed by workshops on interpreting AI outputs and using explainability tools
By visualizing risk criteria outcomes and allowing auditors to see which features drive each risk score
Auditing has always relied on analytical thinking, skepticism, and communication. Now, AI has expanded that toolkit with predictive analytics, anomaly detection, and continuous monitoring. This evolution requires auditors to master *translation* - bridging human reasoning and algorithmic insight.
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