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LinkedIn Learning

Introduction to Auditing AI Systems

via LinkedIn Learning

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Overview

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Learn the fundamentals of how to adapt to and comply with AI regulation in this introductory course on auditing AI systems for bias and discrimination.

Syllabus

Introduction
  • Welcome to the new world of AI audits
1. New Paradigm of AI Audits
  • What is an AI audit?
  • How are audits used?
  • The state of AI legislation
  • Ethics of scoring and classifying humans
2. Why Audit AI Systems?
  • AI audit limitations and opportunities
  • Development workflows
  • AI performance
  • Statistical parity
3. Data for AI Audits
  • Data for auditing AI
  • Sources of bias in data
  • Types of bias and data sampling methods
4. Principles for AI Audits
  • Why explainability matters
  • Levels of transparency
  • Responsible AI principles: Compliance
  • Preparing for AI regulation
5. Model Audits
  • Types of model audits
  • Stages of a model audit
  • Model audit: Home loans
  • Auditing training data
  • Audit outcomes: Explainability statements
  • Continuous audits
Conclusion
  • Generative AI
  • Next steps

Taught by

Ayodele Odubela

Reviews

4.4 rating at LinkedIn Learning based on 471 ratings

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