> ## Documentation Index
> Fetch the complete documentation index at: https://irisdocs.prescientlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Responsible AI in Client Engagements

> Design AI use cases with clear ownership, testing, human oversight and failure handling.

Responsible AI begins with the business process, not a policy document.

## Ask before implementation

* What decision or action will the system influence?
* Who could be harmed if it is wrong?
* What accuracy level is acceptable?
* Who reviews or overrides the output?
* What happens when the model is uncertain?
* What inputs can the system access?
* What tools or downstream actions can it trigger?
* How will performance be monitored after launch?

## Human oversight

Human approval is especially important where:

* the output affects employment, credit, health, safety or rights;
* errors are costly or difficult to reverse;
* model confidence cannot be reliably calibrated;
* the organisation is still learning the workflow.

Do not add a meaningless “human in the loop.” Define what the human checks, when they intervene and what information they receive.

## Testing

Test against representative real-world cases, including:

* common cases;
* edge cases;
* ambiguous inputs;
* adversarial or malformed input;
* failure of external tools or data sources.

## Traceability

Record:

* intended use;
* owner;
* model/provider;
* data sources;
* key risks;
* testing outcome;
* approval status;
* monitoring plan.

## Avoid automation bias

Users may over-trust confident AI output. Training and interface design should reinforce when independent review is required.