Responsible AI Principles: Practical Review Questions
Responsible AI
A principle becomes operational when someone knows what to review, what evidence to retain and who makes the decision.
By MAST Consulting Group · Updated · Implementation guidance
A practical approach
Identify the stakeholders who may be affected by the intended use.
Translate fairness, transparency and oversight into questions appropriate to the task.
Agree evidence and acceptance criteria before evaluating the system.
Record limitations and conditions of approval, including when the review must be repeated.
- 01Question
- 02Evidence
- 03Decision
- 04Review
Put the idea to work
Bring the business owner, technical lead and relevant risk specialists into the same discussion. Start with one real use case and document the information available today. Record missing evidence as an action with an owner, rather than allowing uncertainty to disappear in a summary score.
A useful review output distinguishes facts, assumptions and decisions. It also states what would trigger a reassessment: a new purpose, a changed model, a different data source or an incident. This makes the review useful after the initial meeting.
Make your next AI decision count.
Discuss your ambitions, constraints and next practical steps with MAST.
Apply this to your AI programme
Use this guide with the people who own the business outcome, technical delivery and risk decisions. Define the system boundary, record the evidence available and assign actions for the gaps before approving the next stage.
Plan around your operating location
For regional or cross-border programmes, identify the entities, data flows and accountable owners before deciding which governance requirements apply.