Ethical use of generative AI microcourse
CA ANZ
Last updated September 6, 2026
Ethical use of generative AI is a self-paced microcourse from Chartered Accountants Australia and New Zealand and a component of its Certificate in AI Fluency. It examines the ethical problems generative AI creates specifically for the finance sector — bias, privacy, transparency, accountability and legal exposure — and sets them in the wider effects the technology is having on society. It then supplies ethical decision-making principles to apply to those situations, and asks what an individual professional’s part is in balancing innovation against responsibility. It closes on strategies for deploying generative AI responsibly and communicating those choices. It is a paid course, taken online and on demand.
Part 2 of 4 in CA ANZ's Certificate in AI Fluency
- Introduction to generative AI in finance microcourse
- Ethical use of generative AI microcourse (you are here)
- Planning for GenAI implementation in finance microcourse
- GenAI governance blueprint microcourse
What you'll learn
- Ethical challenges generative AI raises for the finance sector
- Bias, privacy, transparency and accountability in AI-generated output
- Legal risks that accompany generative AI use
- Applying ethical decision-making principles to concrete situations
- Strategies for responsible generative AI deployment
- Communicating ethical judgments about AI to colleagues and stakeholders
Frequently asked questions about Ethical use of generative AI microcourse
Who is Ethical use of generative AI microcourse for?
Finance professionals who need a structured ethics grounding for generative AI; for CA ANZ members it provides verifiable ethics CPD, and the content is not tied to one jurisdiction.
Is Ethical use of generative AI microcourse free?
No — Ethical use of generative AI microcourse is a paid course.
Why we suggest this course
For accountants who need an ethics grounding tied to generative AI specifically, rather than general ethics with an AI example bolted on. The named problem areas are the ones that genuinely arise when a model touches financial information, and the course carries them through to decision principles and deployment strategy instead of stopping at the diagnosis.