Addressing Issues in AI Ethics and Governance (Synchronous and Asynchronous E-Learning)
About This Course
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.
What You'll Learn
• Describe ethical issues in data processing such as informed consent, data privacy, reproducibility, data biasness and techniques to anonymise datasets 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.