This course provides a concise overview of the emerging role of CPAs as independent evaluators of artificial intelligence systems. Participants will explore how established accounting competencies including professional skepticism, assurance, internal controls, risk assessment, and evidence evaluation can be applied to AI. The course introduces major AI standards and frameworks, common AI failure modes, evaluation techniques, governance considerations and opportunities for CPAs to provide credible AI assurance and advisory services.

Prerequisite Knowledge 
A general understanding of accounting, auditing, internal controls, or risk management is helpful; no advanced AI or data science knowledge is required. 

Topics Covered: 

• The emerging CPA role in AI system evaluation 
• NIST, ISO, SOC 2, COSO, and AI assurance frameworks 
• AI regulatory and compliance developments 
• Hallucination, bias, model drift, sycophancy, and security risks 
• AI benchmarks, golden datasets, red-teaming, and performance testing 
• AI governance, controls, documentation, and evidence 
• AI evaluation engagements, independence, ethics, and professional liability

Upon completion of this course, you will be able to:

• Explain the emerging role of CPAs as AI system evaluators 
• Identify major risks and failure modes associated with AI systems 
• Apply established AI risk and assurance frameworks to evaluation engagements 
• Analyze AI performance using appropriate benchmarks and testing methods 
• Evaluate AI governance, controls, documentation, and vendor evidence 
• Assess regulatory, ethical, independence, and liability considerations 
• Design a structured approach for evaluating AI systems throughout their lifecycle