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
Course Resources
Week 1 - Foundation
The main modes of working (14:43)
Spotting risks (11:34)
Researching solutions (9:23)
Effective communications (13:02)
Exercise 1: Putting your knowledge to work (5:26)
Specialized tasks
Overview of non-technical tasks (27:22)
Algorithms, AI and learning machines (21:01
Bias testing (21:41)
Exercise 2: Finding your niche
What now? (3:26)
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