Responsible AI: From Policy to Practice

Policies need processes

Many organizations publish AI principles such as fairness, transparency and accountability. They only matter if teams know what to do on Monday morning. Responsible AI becomes real when principles are tied to approvals, checks and named owners.

A workable starting model

• Inventory: keep a list of AI tools and use cases, including those bought from vendors.
• Classify: rate each use case by risk, for example by the data used and the impact on people.
• Review: apply deeper checks to higher-risk uses, covering data, bias, security and human oversight.
• Approve: define who signs off before launch and who owns the system afterwards.
• Monitor: revisit performance and incidents, and retire or fix systems that drift.

Do not forget people and contracts

Train staff on acceptable use, especially for generative AI tools. Update vendor contracts to cover data use, intellectual property, transparency and responsibility for errors.

Keep up with the rules

AI regulation and standards are developing in many countries. Build a process that can absorb new requirements, and check current rules for each market where you operate.