CPAs as AI Systems Evaluators - Oklahoma Society of CPAs

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Overview

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, regulatory developments, and opportunities for CPAs to provide credible AI assurance and advisory services.

This event may be a rebroadcast of a live event and the instructor will be available to answer your questions during the event.

Highlights

The major topics that will be covered in this course include:

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

Prerequisites

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

Designed For

This course is designed for CPAs, auditors, controllers, finance leaders, risk professionals, and accounting advisors seeking to understand and participate in the emerging field of AI evaluation and assurance.

Objectives

After attending this presentation, 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