Introduction to Auditing AI Systems
1h 19mBeginner2023-09-19
Authors

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.
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
0. Introduction
- 01 - Welcome to the new world of AI audits
1. New Paradigm of AI Audits
- 02 - What is an AI audit
- 03 - How are audits used
- 04 - The state of AI legislation
- 05 - Ethics of scoring and classifying humans
2. Why Audit AI Systems
- 06 - AI audit limitations and opportunities
- 07 - Development workflows
- 08 - AI performance
- 09 - Statistical parity
3. Data for AI Audits
- 10 - Data for auditing AI
- 11 - Sources of bias in data
- 12 - Types of bias and data sampling methods
4. Principles for AI Audits
- 13 - Why explainability matters
- 14 - Levels of transparency
- 15 - Responsible AI principles - Compliance
- 16 - Preparing for AI regulation
5. Model Audits
- 17 - Types of model audits
- 18 - Stages of a model audit
- 19 - Model audit - Home loans
- 20 - Auditing training data
- 21 - Audit outcomes - Explainability statements
- 22 - Continuous audits
Conclusion
- 23 - Generative AI
- 24 - Next steps
Related courses
- Algorithmic Auditing and Continuous Monitoring
- Excel: Introduction to Formulas and Functions (2023)
- Excel: Introduction to Formulas and Functions
- Excel: Introduction to Formulas and Functions (2018)
- Introduction to Security Information and Event Management (SIEM)
- Introduction to AWS Threat Detection
- Introduction to SQLite
- Introduction to AR with Unreal and Xcode for Developers
Related learn paths
- Applying AI as a Tech Leader
- Mastering Responsible AI: From Concept to Auditing
- Understanding Generative AI for Tech Leaders
- Getting Started with Microsoft Excel
- Prepare for the Excel Associate - Microsoft Office Specialist Exam for M365 Apps (MO-210)
- Advance Your Skills with Excel Formulas and Functions
- Become a Six Sigma Green Belt
- Explore Cybersecurity Careers