Foundations of Responsible AI

In this course, Vilas Dhar—an entrepreneur, technologist, and human rights advocate—transforms responsible AI from abstract principles into practical engineering decisions and shows you ways to directly shape AI system behavior and outcomes. Find out how early integration of key practices prevents costly system rebuilds and reputation damage. Learn how to integrate monitoring, bias detection, and transparency from the start. Examine critical architecture and design decisions that shape system behavior. Then explore how to integrate responsible AI practices into existing development workflows without creating bottlenecks. Plus, go over risk assessment frameworks that generate solutions rather than just identifying problems, with practical tools for evaluating AI system risks and making informed trade-off decisions.

Learning objectives
Identify specific points in the AI development process where technical decisions have ethical implications.
Select appropriate model architectures and deployment strategies that align with responsible AI principles.
Implement practical monitoring and testing approaches that catch bias and other issues early.
Integrate responsible AI considerations into existing development workflows without creating bottlenecks.
Apply risk assessment frameworks that generate solutions rather than just identifying problems.
Translate ethical concerns into technical requirements that teams can implement.