Ethics and Critical Thinking in an AI World

Ethics and Critical Thinking in an AI World is a highly interactive, real-world program designed for accounting and finance professionals who are integrating artificial intelligence into their work. Attendees will examine ethical frameworks such as Kidder’s Nine Checkpoints and classic principles like utilitarian, rule-based, and care-based reasoning—applying them to practical cases where professional judgment meets advanced technology. Through relatable case studies, participants will learn how to balance innovation with accountability, strengthen ethical awareness in AI-assisted environments, and apply professional skepticism to maintain integrity in financial reporting.

Who Will Benefit

• CPAs and Internal Auditors seeking to strengthen ethical decision-making in AI-enabled environments.
• Finance and FP&A Managers responsible for interpreting AI-based reports and ensuring reliability in projections.
• Controllers and Accounting Leaders interested in integrating AI tools responsibly while maintaining governance and compliance.
• Ethics Officers and Risk Managers aiming to lead cultural and procedural change toward transparent AI adoption.

Key Topics

• Understanding ethical risks introduced by AI in accounting, auditing, and finance functions.
• Applying Kidder’s Nine Checkpoints framework to real-world financial ethics dilemmas.
• Recognizing and managing bias, data reliability, and transparency concerns in AI-generated financial outputs.
• Reviewing the intersections of professional skepticism, judgment, and artificial intelligence.
• Examining the “dark side” of AI, including potential for fraud, bias, and data misuse.
• Integrating human ethical reasoning with AI-enabled analytics to preserve trust and accountability.
• Using case studies to apply ethical principles in pricing, forecasting, and audit scenarios.

Learning Outcomes

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

• Apply Kidder’s Nine Checkpoints for Ethical Decision-Making to analyze and document the steps taken in resolving AI-related ethical dilemmas.
• Determine when an AI-driven scenario presents a right-versus-wrong issue versus a right-versus-right ethical paradigm using structured ethical tests.
• Compare machine-generated audit or financial outputs with human professional review to identify discrepancies and assess reliability.
• Distinguish between ethical decision-making frameworks—ends-based, rules-based, and care-based—and select the most appropriate for a given corporate scenario.