Level: Beginner to Intermediate
Prerequisite: None
The extraordinary capabilities of large language models, machine learning, and other AI technologies have transformed how we process information, generate insights, and make decisions. While these tools offer remarkable efficiency and innovation, they come with inherent challenges: vulnerability to data inaccuracy, potential for bias, and outcomes that—though mathematically sound—may fail ethical or practical standards.
The critical question isn't whether to employ AI, but how to implement it responsibly.
Problems only occur when there’s a lack of review, transparency, measurement, and auditability. In this course, Evan Levy will cover the fundamentals of AI governance including AI risk levels, the core components, real-world use cases, and how organizations have addressed and reduced their risks associated with artificial intelligence.
You Will Learn
- What is AI governance and why it is necessary
- The existing and perceived risks associated with AI
- Core components of effective AI governance structures
- Real-world case studies of governance challenges and solutions
- Actionable strategies organizations have deployed to mitigate AI risks while maximizing benefits
Geared To
- AI and ML developers
- AI and ML professionals
- Business analysts and managers
- Program leaders
- Non-technical data users