Responsible AI at Work: Putting Ethical AI in Action
About This Course
Responsible AI is becoming essential in the workplace—especially as more teams rely on AI tools to draft emails and reports, summarise documents, support decisions and automate routine tasks. Used well, AI can significantly improve productivity and speed. Used carelessly, it can introduce risks such as inaccurate outputs, privacy breaches, bias, and poor decisions caused by over-reliance on AI.
This hands-on 2-day course is designed for professionals with no coding background. You will gain practical knowledge and experience to use AI more effectively at work—while ensuring your use is ethical, safe, and aligned with organisational and regulatory expectations. The course is grounded in IMDA’s AI governance principles, covering: transparency, explainability, repeatability/reproducibility, safety, security, robustness, fairness, data governance, accountability, human agency and oversight, inclusive growth, and societal and environmental well-being.
The course breaks down these governance principles into clear and relatable workplace scenarios. You will learn how responsible AI differs from simply “using AI,” and how to embed responsible practices into daily workflows—so you can gain productivity benefits without increasing risk.
Through interactive exercises and hands-on labs, you will learn how to identify suitable tasks for AI, set practical guardrails, and redesign work processes to be both efficient and responsible—without needing any coding skills.
What You'll Learn
After completion of the course, you will be able to:
1. Identify high-value workplace tasks where AI can improve productivity—and recognise situations where AI should not be used (e.g., safety-critical or sensitive decisions without proper controls).
2. Potential consequences for unsafe use of AI in workplace
3. Apply Infocomm Media Development Authority (IMDA)’s AI governance principles to workplace AI use, including transparency, explainability, repeatability/reproducibility, safety, security, robustness, fairness, data governance, accountability, human agency and oversight, inclusive growth, and societal and environmental well-being.
4. Use practical techniques to improve output quality and reliability (e.g., verification steps, source-checking, documentation, and reproducible workflows) to support consistent results.
5. Manage common risks in workplace AI use—confidentiality, bias, misinformation, prompt injection/social engineering risks, and automation bias—using simple guardrails, checklists, and human review steps.
Entry Requirements
No technical knowledge or skills are required.