Lesson 9 of 10
Responsible AI, privacy, and deployment
Before using AI in a real process, identify who may be affected and what happens when the system is wrong. Check data permissions, privacy, security, accessibility, bias, and applicable policy. Give people a way to question consequential outcomes and route uncertain cases to a qualified reviewer.
Operate the model safely
- Validate inputs and constrain model actions and tool access.
- Test representative cases, edge cases, and relevant groups before release.
- Monitor quality, drift, errors, latency, and cost after deployment.
- Log only what is needed, protect logs, and define retention periods.
- Provide a fallback and a way to disable the system if harm or failures emerge.
Try it: Choose a low-risk AI use case and list one potential harm, one preventive safeguard, and one signal you would monitor.