- Course
- AI
Developing Technical Audits for AI
The legal and regulatory landscape surrounding AI around the globe is rapidly evolving. This course will teach you about some of the laws, governance, and principles used to evaluate fairness and bias in AI.
What you'll learn
Engineers and regulatory bodies are being introduced to data management, processing, and generation capabilities at an unprecedented rate. The need to approach this new landscape as a responsible AI champion has never been greater. In this course, Developing Technical Audits for AI, you’ll discover how to audit for "responsible AI" principles through evaluation and detection techniques that test principles like fairness, transparency, accountability, safety, and privacy throughout the entire AI lifecycle. First, you’ll explore the regulatory landscape, becoming familiar with laws and policies governing audits and assessments. Next, you’ll see how to apply some of these techniques in a small case study. Finally, you’ll learn how to find and utilize auditing frameworks like Fairlearn. When you’re finished with this course, you’ll have the skills and knowledge of an AI audit engineer needed to apply the NIST AI Risk Management Framework (RMF) to manage and monitor AI risks.
Table of contents
About the author
With over 20 years of experience in software design and development, I am a cloud engineering expert and leader at my company, where I manage complex and innovative solutions for the global insurance provider. As an Associate Vice President and Senior Principal Cloud Engineer, I oversee the architecture, design, infrastructure as code, DevSecOps, leadership, and innovation of cloud-based applications and services, using Microsoft Azure as the main platform. I love to share my knowledge, and I am a lifelong learner.