Introduction
The proper governance of Artificial Intelligence (AI) and autonomous decision-making systems involves addressing many ethical issues such as data privacy, biasness, fairness and explainability. In this introductory course, learners will understand what each of these issues involve and how they relate to the notion of good AI governance. Interesting real-world case studies will be presented to help contextualise these ethical challenges in different industries. The course will also discuss techniques that can help address issues of data privacy, biasness and explainability, both at the stage when data is being prepared and during the process of AI model training. On completion of this course, learners will have acquired the useful AI governance skills and know-how that will help them design, evaluate and deploy Responsible AI solutions in their work place.
This course is credit-bearing (3 AU) and stackable to:
- Graduate Certificate in Responsible Applied Artificial Intelligence (6 AU)
- FlexiMasters in Responsible Applied Artificial Intelligence (15 AU)
- Master of Computing in Applied Artificial Intelligence (30 AU)
Learners will receive their Statement of Accomplishment (for a grade of D and above) or Certificate of Participation for this course—dependent upon their assessment performance.
This course is part of the FlexiMasters in Responsible Applied Artificial Intelligence.
This course is offered as part of the Certificate in AI Ethics and Governance programme and is not available for registration as an individual course.
The Certificate in AI Ethics and Governance programme is offered in collaboration with the Singapore Computer Society (SCS), who will reach out to applicants to provide more information about this certification.
At the end of the course, learners will be able to:
- Describe ethical issues in data processing such as informed consent, data privacy, reproducibility, data biasness and techniques to achieve K-anonymity and combating bias in the data.
- Describe the different notions of fairness and develop a smart task allocation algorithm that balances efficiency and fairness considerations.
- Describe ethical risks in the training of data for AI systems and the use of techniques like federated learning to enhance data privacy during training.
- Describe the different levels of machine learning algorithms and be able to relate them to the governance issue of AI explainability and techniques for interpretable explanations in autonomous decision-making systems.
- Describe Singapore's AI governance practices and guidelines in various industry sectors like finance and healthcare.
Standard Course Fee: S$6540
| SSG Funding Support | BEFORE funding & GST | AFTER SSG funding (if eligible under various schemes) & 9% GST |
| Course Fee | Course Fee Payable | |
| Singapore Citizen (SC) and Permanent Resident (PR) (Up to 70% funding) | $6,000.00 | $1,962.00 |
| Enhanced Training Support for SMEs (ETSS) | $6,000.00 | $762.00 |
| Singapore Citizen aged ≥ 40 years old SkillsFuture Mid-career Enhanced Subsidy (MCES) (Up to 90% funding) | $6,000.00 | $762.00 |
- NTU/NIE alumni may utilise their $1,600 Alumni Course Credits for this course. Click here for more information.
- Learners can utilise their SkillsFuture Credits for this course.
- Singaporeans aged 40 years and above are able to use their SkillsFuture Credit (Mid-Career) top-up of $4,000 to offset the course fee after SSG funding.
- AI Ethics and Governance Fundamentals (2 AU)
- Responsible Generative AI and Applications (3 AU)
- Addressing Issues in Generative AI System Design and Development (2 AU)
- Human-centred Artificial Intelligence User Experience and Data Visualisation (3 AU)
- Responsible Agentic Artificial Intelligence and Applications (2 AU)
