· 4 min read
Responsible AI is often treated as a policy document. In practice it is a set of habits and decisions made by many people, which is why training matters. This checklist can help you scope it. It is general guidance and not legal advice.
Core topics for every audience
- What AI systems can and cannot do reliably, including typical failure modes.
- Acceptable use: what data may be used, with which tools, and for what purposes.
- Human oversight: when output must be reviewed and who is accountable.
- How to raise concerns and report incidents.
Additional depth for risk and compliance teams
Risk, legal and compliance colleagues benefit from a structured view of AI-specific risks, an approach to classifying use cases, and an introduction to the standards and regulatory expectations relevant to your sector. Interpretation of specific obligations should stay with qualified counsel.
Additional depth for delivery teams
Product and engineering teams need to know how to run a use case risk assessment, document decisions, test for reliability and bias, and monitor systems after release.
Make it operational
Governance sticks when it is part of existing delivery processes. Aim to leave each group with a concrete artefact, such as a risk assessment template or a review checklist, that they will use the following week.