As organizations worldwide integrate artificial intelligence into their operations, ensuring ethical and responsible use is essential. This course provides finance and accounting and auditing professionals with the insights and practical tools needed to implement the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF). Participants will explore the frameworkâs principles of accountability, transparency, and ethical AI, learning how to assess and manage risks unique to their field of practice. Using real-world case studies and interactive activities, attendees will develop strategies to mitigate bias, enhance data privacy, and ensure compliance while leveraging AI to drive innovation and growth.
Learning Objectives
- Understand the core principles and functions of the NIST AI Risk Management Framework (AI RMF)
- Identify and evaluate AI-related risks specific to finance and accounting and auditing, including data privacy, model bias, and operational vulnerabilities
- Apply the NIST AI RMF to implement governance structures, risk management strategies, and monitoring processes for AI systems in finance
- Analyze and enhance the trustworthiness of AI systems by focusing on accountability, transparency, fairness, and security
- Develop practical approaches to align AI systems with ethical guidelines, regulatory requirements, and organizational goals
Major Topics
- Introduction to the NIST AI RMF: overview and objectives; importance of managing AI risks in finance and accounting and auditing; and comparative analysis with global standards, such as the EU AI Act
- Core Components of the NIST AI RMF: governance (establishing accountability for AI systems), mapping (identifying risks across the AI lifecycle), and measuring (developing metrics for evaluating risk)
- Applying the NIST AI RMF to Finance and Accounting: strategies for identifying, assessing, and managing risks unique to finance and accounting and auditing; practical approaches to data governance, model validation, security controls, and incident response; and monitoring and adapting AI systems to align with evolving technologies and regulatory requirements
- Trustworthy AI Characteristics: accountability and transparency in financial operations; addressing privacy, security, and ethical concerns; and ensuring fairness and reliability in AI applications
