Introduction to Auditing AI Systems

Introduction to Auditing AI Systems

1h 19mBeginner2023-09-19

Authors

Ayodele Odubela

Ayodele Odubela

Data Scientist and AI Ethicist

Course details

AI regulation is here, so you may be wondering how to adapt. And if you’re an enterprise organization, startup, or AI practitioner, you probably already know that there are very few existing training resources for technical teams. In this course, instructor Ayodele Odubela gives you a hands-on overview of how to assess AI for bias and discrimination to create fairer AI systems.

Explore the fundamentals of the latest regulations governing the use of AI technology, as well as how to navigate the different phases of AI audits in technical terms. Learn how federal discrimination laws can impact AI systems, how to audit both high- and low-risk AI, and how to collect, develop, or purchase benchmark data for auditing and policy review. Ayodele shows you the basics of calculating model fairness and what principles to prioritize and why, including explainability, transparency, compliance, and documentation. Upon completing this course, you’ll be more aware of how to use generative AI tools to mitigate algorithmic bias.

Skills covered

Introduction toArtificial Intelligence FoundationsArtificial Intelligence (AI)

Concepts

Introduction

  • Welcome to the new world of AI audits

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

Why Audit AI Systems

  • AI audit limitations and opportunities
  • Development workflows
  • AI performance
  • Statistical parity

Data for AI Audits

  • Data for auditing AI
  • Sources of bias in data
  • Types of bias and data sampling methods

Principles for AI Audits

  • Why explainability matters
  • Levels of transparency
  • Responsible AI principles - Compliance
  • Preparing for AI regulation

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
40,000 Toman