Foundations of Trustworthy AI

This unit introduces the historical development, core terminology, and principal model types of artificial intelligence while situating AI use cases in law and public service contexts. It examines ethical questions, sources of bias, fairness tradeoffs, and reliability and robustness challenges, and presents leading practices to mitigate harms and improve trustworthiness, preparing students for deeper legal, technical, and applied work in subsequent units.

Learning Objectives

• Analyze the historical evolution and key terminology of artificial intelligence and categorize common AI model types and their use cases relevant to law and public service
• Evaluate ethical dilemmas and the nature and sources of bias in AI systems, comparing fairness tradeoffs across realistic scenarios
• Apply leading practices to identify, mitigate, and document bias and fairness issues in hypothetical AI workflows used in public safety and legal contexts
• Demonstrate understanding of robustness and reliability concepts by analyzing factors that affect generalizability and resilience, and proposing strategies to improve system reliability against failures and adversarial actions