Introduction to Responsible AI
Responsible AI asks whether a system is useful, fair, safe and accountable in its actual setting. These questions should influence data selection, testing and product decisions before launch.
#Historical principles and current guidance
The source lesson discusses Google's seven principles published in 2018: social benefit, avoiding unfair bias, safety, accountability, privacy, scientific quality and appropriate uses. Treat that list as historical course context. Google's published principles evolve; consult the current policy rather than presenting an older list as the latest wording.
#Test concrete risks
A recruiting assistant might favor patterns present in historical hiring data. A loan-related summary might omit a qualification. A document assistant might expose personal information through retrieval or logs. A model's confidence does not resolve any of these risks.
| Risk | Useful check |
|---|---|
| Unfair outcomes | Compare errors across relevant groups with suitable consent and data |
| Unsupported decisions | Require traceable evidence and a review route |
| Privacy leakage | Minimize data, enforce access and test cross-user retrieval |
| Unsafe actions | Apply permissions and approval at the execution boundary |
#Put accountability in the workflow
Tell users what the system does and what it cannot establish. Keep human review where the consequences require it. Provide a way to correct an error and investigate a disputed result.
For a learning exercise, use synthetic examples rather than private student or customer records. Document assumptions and test limitations. Avoid broad claims such as “bias-free” or “no hallucinations”; report the actual cases tested and failures found.
