Bias, Accuracy, and the Statistics of AI Testing
This course focuses on the actual practice of algorithm audit and assurance. The field of regulatory assurance has a long history, and most of the standards from that field can and should be applied in the context of algorithmic systems. However, the complexity and rapidly evolving nature of modern algorithmic systems that utilize AI/ML present new challenges for audit and assurance practitioners; this course focuses on how BABL navigates these challenges.
Bias, Accuracy, and the Statistics of AI Testing
This course focuses on the actual practice of algorithm audit and assurance. The field of regulatory assurance has a long history, and most of the standards from that field can and should be applied in the context of algorithmic systems. However, the complexity and rapidly evolving nature of modern algorithmic systems that utilize AI/ML present new challenges for audit and assurance practitioners; this course focuses on how BABL navigates these challenges.
Format
Language: English
Modality: E-learning
Format
Language: English
Modality: E-learning
Learning Objectives
By the end of the course, you will be able to:
Learning Objectives
By the end of the course, you will be able to:
Know the language of statistical testing for AI/ML systems
Understand different elements of AI testing, validation, and monitoring
Know when to ask critical questions when evaluating/auditing other people’s testing
Designed for
Certification
& Exam
Certified Bias, Accuracy, & the Statistics of AI Testing Professional (by BABL AI)
No exam, just quizzes
What’s Included?
