The Copilot answer that needs the closest attention may look fluent and well organized. It can still label a colleague unfairly, repeat a detail that should have stayed private, or state that an approval happened when it didn't. A confident tone makes these errors easy to miss.
This module covers the judgment the rest of the academy relies on. You'll use Microsoft's six responsible AI principles as questions for reviewing an answer. You'll also learn the limits of the statement "your data isn't used to train the models" and check each claim before it reaches something you send, share, or use to make a decision.
This isn't compliance training, and the three lessons won't make you a privacy lawyer or a security engineer. They will help you decide which answers need more scrutiny and which are ready to move forward. Each lesson ends with prompts and a drill you can run today.
What you'll walk away with
Apply the six responsible AI principles as a practical habit for reviewing real answers
Separate the five data objects in a Copilot interaction and know what protects each one
Distinguish training exclusion, encryption, retention, and memory as four separate protections
Tag a Copilot claim as supported, contradicted, or not in source before you rely on it
Read Microsoft's transparency artifacts for exactly what they document, without over-reading them
Map, measure, and mitigate harm before you lean on an AI assistant for real decisions
