Course Details
Course Details
What You'll Learn
Topic 1: Introduction to AI Ethics and Governance frameworks
- What is AI Ethics?
- Responsible AI and Explainable AI
- Principles of AI Ethics and Responsible AI
- AI Governance Framework
- AI Ethics Use Cases
Topic 2: Applying AI Ethics and Governance frameworks
- Assessment of AI guidelines for organisations
- AI governance processes
- AI Ethics Code of Conduct - bias, privacy, safety
- Case Studies of apply AI Ethics and Governance on AI projects
Assessment
- Written Exam
- Case Study
Course Info
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Funding Eligibility Criteria
Self-sponsored learners (individuals)
- Singapore Citizens and Singapore Permanent Residents aged 21 and above.
- Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
- Singapore Citizens aged 25 and above can pay the remaining fee with SkillsFuture Credit.
Company-sponsored learners
- Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
- Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
- Employers can also claim Absentee Payroll funding for employees who attend during working hours.
- Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).
Conditions for all funded learners
- Attendance is taken digitally with Singpass for every session, including the assessment.
- You must complete the course and pass all assessments (assessed as Competent).
- If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
- 9% GST is charged on the full course fee before funding.
- Promo and discount codes cannot be used on funded courses.
- Any balance after funding can be paid in cash, with SkillsFuture Credit, with PSEA (Singapore Citizens with a PSEA), or partly reimbursed by UTAP for NTUC members.
How to Apply SkillsFuture Credit Claim for Individuals
Singapore Citizens aged 25 and above can use their SkillsFuture Credit (base tier) to pay the course fee after funding. The $4,000 mid-career top-up cannot be used for this course.
- Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
- We email you an invoice showing the course fee after funding.
- From 60 days before the course starts, and no later than the course start date, log in to MySkillsFuture with Singpass and click Claim credits.
- Click Submit a Claim and search for this course by its course code, TGS-2023021102.
- Select your class start date, enter the course fee payable after funding and the amount of credit to claim, then upload the invoice (PDF, JPG or PNG, under 4MB).
- Review, accept the terms and click Agree and Submit. The credit is paid directly to us; processing can take up to 14 working days.
Changing or withdrawing: cancel the claim on MySkillsFuture at least one day before the course starts. If your class date changes, cancel the claim and submit a new one with the new date.
How to Apply SkillsFuture Enterprise Credit (SFEC)
SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.
Current SFEC: classes ending on or before 30 November 2026
- Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
- SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.
- Check your company's SFEC eligibility and balance on EnterpriseSG.
- Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
- Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
- No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.
Any unused balance of the current SFEC expires on 30 November 2026.
Redesigned SFEC from 1 December 2026
Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.
How to Apply UTAP (NTUC Members)
NTUC members can claim 50% of the unfunded course fee under the Union Training Assistance Programme (UTAP), capped at $250 a year (aged below 40) or $500 a year (aged 40 and above). The cap is shared with UTAP claims for AI tool subscriptions.
Eligibility: you must be an NTUC member paying full membership fees, with your membership paid up before and during the course and when you claim. The course must not be fully paid for by your employer or another source.
- Make sure your NTUC membership is paid up before the course starts.
- Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
- Attend at least 75% of the course and sit all assessments.
- Within 6 months after the course ends, go to the NTUC U Portal, open UTAP under E-services, log in with Singpass and choose Course Fee Claim.
- Enter the nett course fee you paid (after all subsidies and SkillsFuture Credit, including GST), and upload your invoice or receipt and your certificate or result slip (PDF, JPG or PNG, up to 5MB each).
- NTUC usually pays the claim within 4 weeks of submission.
Read more in our UTAP guide.
How to Apply PSEA
Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.
- Check that this course shows the PSEA tag on its MySkillsFuture page.
- Check your PSEA balance on MOE's Student Finance System with Singpass.
- Download and complete the PSEA withdrawal form, then submit it to us.
- We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
- MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.
More details: MOE PSEA overview.
Appeal Process
If you disagree with an assessment decision, you have the right to appeal.
- When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
- Discuss the decision with the assessor or the assessment manager first.
- If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
- Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
- An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
- You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.
Job Roles
Job Roles
- AI Researcher
- Machine Learning Engineer
- AI Product Manager
- Data Scientist
- Ethical Compliance Officer
- AI Policy Advocate
- Technology Ethicist
- Chief Technology Officer (CTO)
- AI Strategy Consultant
- AI Governance Specialist
- Data Privacy Officer
- Human-Centered AI Designer
- AI Educator and Trainer
- Tech Start-up Founder
- AI Regulation and Policy Advisor
Trainers
Trainers
Responsible AI is more than a teaching topic for Dr. Alfred Ang; it shapes how he designs the systems he builds. He holds a Certificate in AI Ethics and Governance (Senior Professional) from NTU and is an IBM certified instructor for the AI Practitioners course, and his professional approach emphasises applied skills, ethical AI governance and sustainable digital adoption. A full-stack AI developer and Agentic AI solution architect, he is Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, where he leads agentic AI adoption and digital transformation initiatives that help organisations move from experimentation to operational AI. His other credentials include a PhD in Electrical Engineering from NUS, the Microsoft Certified AI Engineer Associate (AI-102) and Data Scientist (DP-100), AWS Certified AI Practitioner, CompTIA Security+, PMP and ITIL. His GitHub portfolio offers concrete governance examples: n8n-hrportal, an HR portal whose OpenAI chatbot is guarded by input and output guardrails and whose leave approvals keep a human manager in the loop; AICoaching, a coaching simulator for redesigning jobs around accountable AI-agent ownership; and AIInterviewing, which scores practice interviews against role-specific rubrics. He is an ACTA-certified trainer and DACE-certified curriculum developer, and has published more than 20 peer-reviewed papers and co-invented more than 20 US patents. He holds an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, and his career spans the defence, electronics and semiconductor industries as well as education.
In WSQ - Fundamentals of AI Ethics and Responsible AI, Dr. Ang helps learners turn principles into practice. The first topic introduces AI ethics and governance frameworks: what AI ethics means, the ideas behind responsible and explainable AI, the core principles of responsible AI, AI governance frameworks and real use cases. The second topic applies those frameworks, assessing AI guidelines for organisations, designing AI governance processes and drafting an AI ethics code of conduct covering bias, privacy and safety, before working through case studies of ethics and governance applied to AI projects. Because he builds and deploys AI agents himself, he can show learners where ethical risks actually appear in a project, such as biased data, unclear accountability for an agent's actions, personal data flowing into prompts and outputs nobody reviews, and what practical controls look like, from guardrails and human approval steps to audit trails and clear ownership. His sessions are discussion-rich and grounded in case studies, encouraging learners to test each framework against their own organisation. As a WSQ course it prepares learners for assessment, and they leave equipped to apply ethical decision-making and governance to real AI initiatives. He also helps learners tailor governance to their organisation's size, so that smaller teams adopt proportionate controls rather than copying frameworks designed for much larger enterprises. To see how he builds guardrails and accountability into his projects, learners can view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
Yeo Hwee Theng is a data science leader and AI strategist with deep expertise in enterprise AI governance, responsible data use and digital transformation. As Data & Analytics Product Lead at Amplify Health, she drives AI adoption strategies that emphasise fairness, data privacy and responsible decision-making. She previously served as AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot, deploying large-scale AI systems for global enterprises. She holds a Master of Technology in Enterprise Business Analytics from the National University of Singapore and an Advanced Certificate in Learning and Performance (ACLP).
In Fundamentals of AI Ethics and Responsible AI, she shows participants how to build ethical thinking into every stage of AI development. Learners first examine what AI ethics means, the principles of responsible and explainable AI, and established AI governance frameworks, illustrated through real use cases. They then apply these frameworks by assessing AI guidelines for organisations, mapping governance processes and working through an AI ethics code of conduct covering bias, privacy and safety, before analysing case studies of ethics and governance on actual AI projects. Combining technical knowledge with governance expertise, she helps learners see how transparency, accountability and regulatory alignment build trust, compliance and long-term organisational value.
Tan Woei Ming is a data scientist and AI engineer with over 15 years of experience in applied machine learning, deep learning and data-driven automation. He has led AI initiatives in predictive analytics and process optimisation across the semiconductor and manufacturing industries, and his work focuses on designing AI systems that are explainable, auditable and aligned with ethical development principles. He holds a Master's in Intelligent Systems from the National University of Singapore and a First-Class Honours in Electrical and Electronic Engineering from Nanyang Technological University, and is known for translating complex AI technologies into practical business applications.
In WSQ - Fundamentals of AI Ethics and Responsible AI, Woei Ming brings a practitioner's perspective to ethical AI. Learners are introduced to AI ethics, responsible and explainable AI, core ethical principles and AI governance frameworks, supported by real use cases. They then apply these frameworks by assessing AI guidelines for organisations, examining governance processes and building a code of conduct that addresses bias, privacy and safety, using case studies of ethics and governance on AI projects. By connecting technical understanding with ethical reasoning, he helps participants embed accountability and transparency into AI work and support trustworthy, fair outcomes in their organisations.
Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in data analytics, machine learning and AI applications. A defining feature of his teaching is its focus on both the opportunities and the risks of emerging technologies. His hands-on work includes developing predictive models and deep learning solutions with R, Python, TensorFlow and Keras, and his professional background spans healthcare, finance and customer analytics, sectors where the responsible use of data matters greatly. He also holds a Masters in Education from NTU and has eight years of teaching experience.
In WSQ Fundamentals of AI Ethics and Responsible AI, Dwight draws on his practical experience of building models to ground ethical principles in real AI work. He introduces AI ethics, responsible and explainable AI, and AI governance frameworks, with use cases that show the principles in action. Learners then apply these frameworks by assessing AI guidelines for organisations, examining governance processes and working through an AI ethics code of conduct covering bias, privacy and safety. Through case studies, participants leave able to identify ethical risks in AI projects and support responsible, well-governed AI adoption in their organisations.
Dr. Alvin Ang is a technology and ethics researcher with extensive experience in artificial intelligence, digital governance and educational technology. He holds a PhD and has worked on projects examining the societal implications of AI adoption, algorithmic bias and responsible data practices. He has designed and delivered academic and professional programmes focused on ethical AI, data privacy and human-centred design, and his research and teaching promote the responsible integration of AI systems across business, education and government contexts.
In WSQ - Fundamentals of AI Ethics and Responsible AI, Dr. Ang explores the intersection of technology, ethics and policy. Learners begin with AI ethics and governance frameworks, including responsible and explainable AI, the principles of AI ethics, governance frameworks and use cases. They then apply these frameworks by assessing AI guidelines for organisations, examining AI governance processes and building an AI ethics code of conduct covering bias, privacy and safety, supported by case studies. His sessions highlight the ethical challenges of automation, data use and generative AI, and encourage critical reflection on human oversight in AI decision-making. Through practical examples, he empowers participants to apply responsible AI principles that align innovation with societal well-being.
Review
Customer Reviews (3)
- Average Rating: 5.0/5 Review by Course Participant/Trainee
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I am happy that i have learnt much from this course. Hope to attend to more courses int he near future (Posted on 3/12/2026)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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. (Posted on 3/5/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - I was very impressed with the content and the instructor, Dwight Fonseka. Review by Course Participant/Trainee
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I was very impressed with the content and the instructor, Dwight Fonseka. The course covered a wide range of topics, its applications, relevance, and importance. Dwight Fonseka was very knowledgeable and engaging. Through his presentations and videos, he did a great job of helping me understand deeper the course. It is a well-designed and informative course that will give you the starting skills needed to be ethically and responsible in AI areas. (Posted on 2/28/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
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