Applied AI Auditing in Python
Applied AI Auditing in Python
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 5m | 153MB
Instructor: Ayodele Odubela
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 5m | 153MB
Instructor: Ayodele Odubela
AI regulation has arrived, and while there are great high-level strides towards operationalizing AI principles, practitioners must adopt the practice of auditing AI systems to meet compliance and transparency standards. This course guides data scientists and machine learning engineers through the technical process of auditing an AI system, step-by-step.
This course is a hands-on, technical course that shows you how to quantify unfairness and disparities to uncover bias and develop fairer AI systems. Instructor Ayodele Odubela explains how to plan, execute, and report on AI audits. Learn the difference between data and algorithm auditing, frameworks for scalable audits, and how context and historical bias play a role in making technical recommendations.
Learning objectives
- Learn how to audit high- and low-risk AI systems
- Collect, design, and mange benchmark data for auditing
- Calculate and perform a disparity analysis
- Determine model fairness with the Fairlearn Python package
- Decide which fairness metric to use
- Make technical recommendations post-audit
Applied AI Auditing in Python
