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Overview
"Foundations of Responsible AI" is a comprehensive course that introduces learners to the principles and practices of building and deploying artificial intelligence (AI) technologies in a responsible and ethical manner. As AI continues to shape industries worldwide, ensuring its ethical application is essential for long-term success. This course focuses on laying a solid foundation for understanding the key ethical issues surrounding AI, such as bias, transparency, accountability, and fairness.
Syllabus
Introduction
- Understanding responsible AI
1. Philosophy of AI
- What is AI and how does data enable it?
- Modern AI development
- Problems in ML that differ from software engineering
2. Data Awareness and Literacy
- Big data and where it comes from
- Seeing trends in data
- Building data understanding
- Visualization and comparing data
- Storytelling with data
3. Ethical Theories
- Introduction to ethical AI
- Ethical frameworks
- Beneficence vs. maleficence
- Calculating consequences
- Consequence scanning
- Common good and equity
4. Responsible AI Principles
- Fairness
- Transparency
- Accountability
- Explanations
- Interpretability
- Inclusivity
5. Algorithmic Harm
- Why fairness related harms?
- Critical AI incidents and learnings
- Bias in the design and development lifecycle
- Causal reasoning and fairness
- Risk mitigation in AI
- Technical aspects of sociotechnical solutions
6. Human Rights and AI
- Anonymity and data privacy
- Unintended uses and misuses
- Unethical business cases
- Autonomous systems and society
- Who AI is developed for?
Conclusion
- AI regulation and applying responsible AI frameworks
via LinkedIn Learning
2 hours 31 minutes
Certificate Available
English
On-Demand
Intermediate
Instructor
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