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AI Legal Governance: Audit, Accountability, & Tradeoffs

University of Illinois at Urbana-Champaign via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

2 weeks, 6 hours a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Part of

AI Governance and Compliance in Law

Overview

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This course is for learners who need to understand, build, or oversee AI governance and compliance programs. It begins by turning governance design into practice: building accountability structures, setting acceptance criteria, error tolerance, and risk tolerance, and addressing shadow AI with the NIST AI RMF. It then covers the audit function, including audit types, model drift, EU conformity assessments, U.S. state audit expectations, and the audit trail. The course closes with the philosophical debate between the precautionary principle and the innovation principle, tracing it through history, U.S. federal and state policy, the EU AI Act, agentic AI, and real-world cases. Throughout, it emphasizes that effective governance should produce adherence rather than avoidance.

Syllabus

Module 1: Implementing AI Governance and Compliance
- You will learn how to turn an AI governance design into a working program by building accountability structures, setting acceptance criteria, error tolerance, and risk tolerance, and addressing shadow AI using the NIST AI RMF. You will also learn how the audit function tests AI systems in practice, including audit types, model drift, EU conformity assessments, U.S. state audit expectations, and the deliverables that make up an audit trail. You will conclude with a case study of how Microsoft has implemented a mature AI governance and compliance framework.
Module 2: Weighing Risk and Progress in AI Governance
- You will learn how the precautionary principle and the innovation principle shape AI governance and compliance, including the control and alignment problem, the vulnerable world hypothesis, and the historical roots of each principle. You will also learn how the balance between the two plays out in U.S. federal executive orders, California legislation, the EU AI Act, agentic AI applications, and earlier technologies such as the printing press, the atomic bomb, and the polio vaccine. You will examine how companies and regulators strike this balance in practice through a frontier AI model release and New York City Local Law 144, and why effective governance should generate adherence rather than avoidance.