WSQ - Fundamentals of AI Ethics and Responsible AI

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

Final Assessment

• Written Assessment - Short Answer Questions (WA-SAQ)
• Case Studies (CS)

Promotion Code

Promo or discount cannot be applied to WSQ courses

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.

Minimum Software/Hardware Requirement

Software: NIL

Hardware: Windows and Mac Laptops

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Steps to Apply Skills Future Claim

• The staff will send you an invoice with the fee breakdown.
• Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
• Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
• Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The  SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

Note

• SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
• All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
• An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
• UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
• UTAP support amount will be paid to training provider first and claimed after end of class by learner.

Appeal Process

1. The candidate has the right to disagree with the assessment decision made by the assessor.
2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
6. If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
9. The assessor can help the candidate with writing and lodging the appeal.
10. he assessment manager will collect information from the candidate & assessor and give a final decision.
11. A record of the appeal and any subsequent actions and findings will be made.
12. An Assessment Appeal Panel will be formed to review and give a decision.
13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
15. Please click the link below to fill up the Candidates Appeal Form.

• 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

Dwight Nuwan Fonseka: 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. He has extensive hands-on experience developing predictive models, RShiny dashboards, and deep learning solutions using R, Python, TensorFlow, and Keras. With a strong professional background in healthcare, finance, and customer analytics, Dwight brings an applied perspective to teaching AI, focusing on both the opportunities and risks of emerging technologies.

In this program, Dwight emphasizes the principles of ethical and responsible AI, including fairness, transparency, accountability, and privacy. Drawing from his applied AI projects, he provides learners with real-world examples of algorithmic bias, data governance issues, and regulatory challenges, while offering frameworks to mitigate these risks. His teaching ensures participants not only understand the ethical foundations of AI but also develop the ability to apply responsible AI practices in organizational and industry contexts.

Dr. Alvin Ang: Dr. Alvin Ang is a technology and ethics researcher with extensive experience in artificial intelligence, digital governance, and educational technology. Holding a PhD in Information Systems, he has worked on projects examining the societal implications of AI adoption, algorithmic bias, and responsible data practices. Dr. Ang has designed and delivered academic and professional programs focused on ethical AI, data privacy, and human-centered design. His research and teaching promote the responsible integration of AI systems across business, education, and government contexts.

In “Fundamentals of AI Ethics and Responsible AI,” Dr. Ang explores the intersection of technology, ethics, and policy, guiding participants through frameworks for fairness, accountability, and transparency in AI systems. His sessions emphasize the ethical challenges of automation, data use, and generative AI, encouraging critical reflection on human oversight in AI decision-making. Through case studies and practical examples, he empowers learners to apply responsible AI principles that align innovation with societal well-being.

Tan Woei Ming: 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. With 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, he has led numerous AI initiatives in predictive analytics and process optimization. His work focuses on designing AI systems that are explainable, auditable, and aligned with ethical development principles.

In “Fundamentals of AI Ethics and Responsible AI,” Woei Ming brings a practitioner’s perspective to ethical AI design and implementation. His sessions cover bias mitigation, model interpretability, and governance structures for responsible AI deployment. By connecting technical understanding with ethical reasoning, he helps learners grasp how to embed accountability and transparency into machine learning pipelines to ensure trustworthy and equitable outcomes.

Yeo Hwee Theng: 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 the Data & Analytics Product Lead at Amplify Health, she drives AI adoption strategies emphasizing fairness, data privacy, and responsible decision-making. Previously, she served as an 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 NUS and an Advanced Certificate in Learning and Performance (ACLP).

In “Fundamentals of AI Ethics and Responsible AI,” Hwee Theng teaches participants how to integrate ethical frameworks into the lifecycle of AI model development. Her sessions emphasize the importance of transparency, stakeholder accountability, and regulatory alignment in AI system design. By combining technical knowledge with governance expertise, she helps learners understand how responsible AI practices can foster trust, compliance, and long-term organizational value.

Teh Siew Yee: Teh Siew Yee is a digital transformation and analytics leader with over 20 years of experience in technology, finance, and manufacturing sectors. He has held leadership roles at Standard Chartered, Hewlett-Packard, and TikTok, managing projects in AI governance, data management, and compliance. A certified ACLP trainer, he holds a Master of IT in Business (Artificial Intelligence) from Singapore Management University and a Bachelor of Engineering from NTU. His professional focus centers on balancing innovation with accountability in AI-driven organizations.

In “Fundamentals of AI Ethics and Responsible AI,” Siew Yee focuses on helping professionals understand the principles of responsible AI adoption within business contexts. His sessions cover risk assessment, governance frameworks, and the operationalization of ethical AI practices. By linking regulatory requirements with practical implementation, he equips learners to design and manage AI systems that uphold transparency, fairness, and societal responsibility.