Responsible Generative AI Practices
About This Course
The Responsible Generative AI Practices course equips learners with the knowledge and tools to address the ethical challenges associated with generative AI systems. Participants will delve into the societal impacts and risks of biased AI algorithms, particularly how they affect different cultural and minority groups. The course covers ethical trade-offs in generative AI, including considerations around environmental sustainability, privacy, and regulatory frameworks. Learners will also gain the skills to develop ethical strategies and implement AI governance principles that promote fairness, transparency, and long-term sustainability. By the end of the course, participants will be prepared to champion responsible AI practices in their organizations, ensuring AI systems are deployed in ways that minimize societal bias and uphold ethical standards.
As AI technology continues to evolve, the ethical implications of its use become increasingly important. This course is highly relevant for professionals working in AI development, data science, and policy-making who wish to navigate the complexities of generative AI responsibly. Understanding how to mitigate bias, ensure privacy, and maintain transparency in AI systems is critical for creating solutions that serve all communities equitably and sustainably.
This course is designed for intermediate learners with a foundational understanding of AI or ethics who are looking to deepen their knowledge in responsible AI practices.
What You'll Learn
LO1: Analyse the societal impacts and ethical risks of biased AI algorithms across different cultural and minority contexts.
LO2: Evaluate ethical trade-offs in generative AI systems, balancing environmental, privacy, and framework considerations.
LO3: Develop ethical strategies and apply AI governance principles to promote fairness, transparency, and sustainability.
LO4: Implement responsible AI practices and champion transparency to reduce societal bias and ethical risks in deployment.
Course Outline:
LU1: Generative AI Ethics Analysis
T1: Implications of biased AI algorithms and potential societal impact
T2: Analyse the ethical implications of AI algorithms (e.g., assess bias and fairness concerns)
T3: Analyse the impact of AI on different societal groups and cultures (e.g., minority groups)
LU2: Generative AI Ethics Evaluation
T1: Environmental impact of generative AI use (e.g., energy consumption)
T2: Evaluate ethical frameworks and guidelines in AI development
T3: Evaluate the trade-offs between privacy and model performance
T4: Assess environmental impact (e.g., estimate energy footprint)
LU3: Generative AI Strategy Development
T1: Ethical frameworks and guidelines for generative AI-powered tools (including legal requirements for data protection)
T2: Ethical considerations in AI development, (e.g., bias, privacy, transparency, accountability)
T3: Develop strategies to integrate ethics into AI development and evaluation of AI systems
T4: Apply ethical guidelines to AI development and use, considering fairness, accountability, transparency and sustainability etc.
LU4: Generative AI Strategy Implementation
T1: Trustworthy and transparent communication of generative model capabilities and limitations
T2: Implement strategies to mitigate bias and ethical risks in AI systems
T3: Champion best practices for responsible and ethical use of generative AI, addressing concerns about transparency, bias, and societal impact
Entry Requirements
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
Target age group: 21-65 years old