A practical checklist for teams building AI products who want to do it right from day one.
Responsible AI is not a checkbox you tick before launch. It is a design practice woven into every sprint. Here is a starter framework our team uses:
- Define harm surfaces early. Who can be harmed? In what scenario? Document these before writing code.
- Establish ground-truth diversity standards. Your labelling team should reflect your user population.
- Red-team with diverse testers. Adversarial testing by people unlike your core team surfaces blind spots fast.
- Build explainability in. If you cannot explain a decision in plain language, the model is not ready to ship.
- Create a feedback loop. Affected communities must be able to report harms after deployment.