Ethics in AI is often sidelined in the process of advancement; however, for many stakeholders, ethical implementation and human-centred AI matter more than advancement itself. This course addresses this, along with principles and frameworks to understand how ethics relates to and can become ingrained in how we adopt artificial intelligence.
This AI Ethics course is Module 1 of CAIAI’s Introduction to Artificial Intelligence course, which is available individually for learners who want to take a deeper look at the subject rather than in a condensed offering. It covers foundational principles of AI ethics, the frameworks that put them into practice, and how ethical failures show up in industry.
The module runs across three chapters:
The specific ground includes AI ethics, the four pillars of ethical AI, deontological ethics, consequentialist ethics, human-centred AI, and deepfake recognition. It is taught with clear industry examples and with attention to how ethical violations get mitigated in practice, not only identified. Learners also analyze ethical dilemmas in the generative AI applications they already use, including ChatGPT and Gemini.
The materials consist of chapter readings from the Introduction to Artificial Intelligence textbook, course videos, and online discussions where you post at least one comment addressing each chapter’s prompt. Chapters 1 and 2 close with a quiz requiring 70% or higher; chapter 3 is assessed through discussion and the module assignment. Nothing is delivered live, so there is no session to miss, and you complete readings and videos at your own discretion.
Ethics has moved from a philosophical topic to an operational one. Statistics Canada found that 19.2% of Canadian businesses used AI to produce goods or deliver services in the second quarter of 2026, triple the 6.1% recorded two years earlier. Decisions that used to be made by people are now being made, or shaped, by systems that need someone to answer for them.
The expectations are being written down. Canada’s federal, provincial and territorial privacy regulators have published joint principles for responsible, trustworthy and privacy-protective generative AI technologies, setting out how existing privacy law applies to these tools.
Meanwhile, the training for ethical AI use at scale has not arrived. Statistics Canada reports that among businesses using AI, only 32.0% provided AI-related training for existing employees. Being the person in the room who can name the ethical problem and propose a workable answer is rare, and the Future Skills Centre reports that workers with AI competencies command a 56% wage premium.
Ethics is the part of CAIAI’s teaching that travels furthest across sectors. We have taught it to university faculty deciding what is acceptable in coursework, nonprofit staff handling information about vulnerable clients, and executives deciding what to automate within their organization. The examples change from one room to the next; however, the four pillars do not. Below are just a few of those engagements:
These articles cover the same ground the module does, and give a fair preview of how it is taught.
Learners or employers are usually unaware of opportunities to pay for their learning. We have shared resources to help you identify available funding opportunities, including scholarships for individuals and grant programs for individuals and employers.
Apply for a scholarship through our Canadian AI scholarships page. In addition, visit our Canadian AI grant funding page to see what individuals or employers can apply for.
You can enrol directly on our learning platform, or use the form below if you want to ask any additional questions.
Our mission is to help Canadians learn, adapt and succeed with AI. Succeeding with AI includes knowing what not to build. This module exists because the ethical questions arrive at the same moment the tools do, and the people asked to answer them are rarely given anything more than instinct to work with.
