Foundations of Trustworthy AI

This unit introduces the historical context, core terminology, and common model types that underpin modern AI systems, and it frames the new technical and ethical challenges that arise in the AI era. Students will explore bias, fairness, robustness, and reliability, and learn leading practices for assessing and mitigating risks to prepare for applied design and governance work in later units.

Learning Objectives

• Analyze the historical progression and foundational terminology of AI to differentiate major model types and describe representative real-world use cases for each
• Apply ethical frameworks to identify sources of bias and evaluate fairness tradeoffs in AI systems, proposing at least two context-appropriate mitigation strategies for a given scenario
• Evaluate reliability and robustness challenges—including generalization limits, brittleness, and adversarial threats—and prioritize mitigation approaches for deployment in a specified application
• Demonstrate leading practices by designing a concise risk assessment and improvement plan that addresses fairness and robustness concerns for an AI system