Foundations in Responsible AI Architectures: Tools, Metrics, and Audits

This technical AI course is best for learners who are closely involved in the development of AI models, like ML/AI engineers and data engineers, as well as technically-fluent product managers, AI governance officers, or similar roles. In reviewing the ML development lifecycle, the potential causes and types of bias, and widely-used metrics, learners will gain a high-level technical understanding of responsible AI.

Key skills covered in this course: 

• Visualizing RAI in the ML lifecycle
• Basic competence with some widely-used methods of bias and fairness assessment

Upon successful completion of the course, learner outcomes include: 

• Place RAI in the ML development lifecycle, enabling ethics-by-design
• List different types of biases that can occur in ML lifecycle in order to proactively address and mitigate them
• Describe and deploy technical approaches to assessing fairness and bias

Potential learners:

• AI Researchers
• Data Engineers
• Data Scientists
• ML/AI Engineers
• Technical Project Managers

Prerequisites: 

• Experience in AI engineering or research