This course provides a practical, end-to-end exploration of responsible AI engineering for developers, researchers, and engineers working with modern AI systems. You’ll move from foundational concepts to applied techniques used to assess, align, and govern AI models in real-world deployments.
The course begins by distinguishing AI safety from AI security and mapping the full spectrum of AI risks, including bias, robustness failures, misalignment, and misuse. You’ll then analyze why models fail by examining technical alignment breakdowns such as reward hacking and specification gaming. Through hands-on exercises, you’ll audit models using adversarial attacks and interpretability tools like LIME and SHAP, apply alignment methods inspired by RLHF and PPO-style optimization, and automate red-teaming workflows with PyRIT.
The course concludes with advanced topics, including evaluating models for dangerous capabilities, implementing runtime governance, and constructing formal AI safety cases.
This course provides a practical, end-to-end exploration of responsible AI engineering for developers, researchers, and engineer...Show More
WHAT YOU'LL LEARN
An understanding of AI safety, AI security, and their roles in responsible System Design
The ability to classify AI risks, including bias, misalignment, and catastrophic misuse
Hands-on experience auditing model robustness using adversarial attacks and interpretability tools
A working knowledge of alignment techniques such as RLHF and PPO-style optimization
Familiarity with red-teaming, runtime governance, and formal AI safety cases
An understanding of AI safety, AI security, and their roles in responsible System Design
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Content
1.
Building the Foundation for Safe AI Systems
4 Lessons
Build a foundational risk map by contrasting accidents with attacks and deconstructing alignment failures such as reward hacking and Goodhart’s Law.
2.
The Technical Toolkit
5 Lessons
Gain technical control by performing adversarial stress tests, auditing opaque decisions with interpretability tools, and steering intent using RLHF.
3.
Advanced Governance and Frontier Problems
5 Lessons
Deploy safe systems by measuring dangerous capabilities, automating red teaming with PyRIT, and governing autonomous agents through runtime frameworks.
Certificate of Completion
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