# A Responsible AI Checklist — Artificial Intelligence

Source: https://www.skillbyai.com/en/artificial-intelligence/e-check

> Questions for any AI project.

## From idea to operation

Before building: is AI the right tool, or would a rule or search algorithm be simpler and more reliable? Is the goal and performance measure clear, including which errors matter most? Is the data lawful, representative and documented? During development: is the system evaluated on realistic data, per group, with uncertainty reported? Is it explainable enough for its use? After deployment: is it monitored, with a way for people to report problems and appeal decisions, and a named owner accountable for outcomes?

## The checklist

Use it at project start and before launch.

```text
[ ] simplest suitable method chosen (rules / search / ML / LLM)
[ ] goal, performance measure and costly errors defined
[ ] data lawful, representative, documented
[ ] evaluated on realistic data, per group, with uncertainty
[ ] explanations or traces appropriate to the stakes
[ ] human oversight for consequential decisions
[ ] privacy and security reviewed (data, prompts, tools)
[ ] monitoring, feedback channel and appeal process
[ ] named owner and documentation (model or system card)
[ ] current regulations checked for this use case
```

## Keep learning

AI moves quickly; revisit methods, risks and rules regularly, and follow the Machine Learning, Deep Learning and LLM courses for depth.

**Quiz:** What should come first in an AI project?

- [ ] Deploying to all users
- [ ] Choosing the biggest model
- [x] Checking whether AI is the right tool and defining the goal and costly errors
- [ ] Skipping evaluation

*Answer:* Checking whether AI is the right tool and defining the goal and costly errors. Start with the problem, not the technology.