Foundations of Trustworthy AI

This unit introduces the historical development of AI, core terminology, common model types, and the new challenges posed by modern AI systems. Students examine ethical questions about bias and fairness and learn concepts and practices related to robustness and reliability to establish a foundation for later modules on mitigation, governance, and deployment.

Learning Objectives

• Analyze major milestones and drivers in the development of machine intelligence and categorize common AI model types with corresponding business use cases
• Apply core AI terminology to explain model architectures, capabilities, limitations, and emergent behaviors in real-world examples
• Evaluate sources and mechanisms of bias, tradeoffs in fairness, and leading practices for promoting equitable outcomes in AI systems
• Demonstrate methods to assess robustness and reliability, identify common vulnerabilities and failure modes, and propose appropriate mitigation strategies