What you’ll learn
This course works covers topics in technical testing of AI and algorithmic systems needed for auditors. Topics covered include:
Performance metrics for algorithmic systems
Quantitative
Qualitative
Sampling and estimation
Definition and types of bias and fairness
This course is part of a 5-course certification program for AI and Algorithm Auditors. Anyone can take the course and get a certification after an exam and exit interview.
About the Instructor
Shea Brown is the founder and CEO of BABL AI, a research consultancy that focuses on the ethical use and development of artificial intelligence. His research addresses algorithm auditing and bias in machine learning, and he serves as a ForHumanity Fellow that sets standards for the organizational governance of artificial intelligence.
He has a PhD in Astrophysics from the University of Minnesota and is currently an Associate Professor of Instruction in the Department of Physics & Astronomy at the University of Iowa, where he has been recognized for his teaching excellence from the College of Liberal Arts & Sciences.
Curriculum
Introduction
What you'll learn (9:01)
Course Resources
Week 1 - Foundations
The sociotechnical algorithm (8:30)
Inputs, outputs, and parameter spaces (12:25)
Statics, probability, and sampling (27:36)
Exercise 1: Putting your knowledge to work
Week 2 - Metrics & Testing
Bias testing: general strategy (18:46)
Basic performance metrics (26:28)
Robustness, and validation (18:38)
Examples (31:03)
Exercise 2: Case study
Don’t just take our word for it
Choose a Pricing Option
Bias, Accuracy, and the Statistics of AI Testing
Essential Tools for AI and Algorithm Auditing
Additional qualifying discounts are available
Contact us today to learn more