AI Governance
ai-governance
AI Engineering
Make human oversight meaningful
Examine a fictional AI coach whose feedback passes a human Approve button, calculate what careful review would cost, and propose the purpose, data, review, and ownership controls that make the oversight meaningful.
Specify a review and escalation workflow
Specify how disputed AI feedback is re-reviewed, corrected, and escalated, with server-side authorization, artifact-bound approval, deadlines, and concurrency rules, then prove the rules with acceptance fixtures.
Govern AI outputs that may be repurposed
Decide what happens when a partner repurposes formative AI practice ratings for candidate screening, and design the authority, enforceable controls, evidence, and remedy that make the decision hold.
Set team rules for AI tools and company data
Write the rules a 30-person accounting team follows when it uses AI tools with client data. Classify the data, match each class to a tool by its data terms, give alternatives and an exception path, and handle client data that staff already pasted.
Lead the response to AI errors that reached customers
Lead the response when a fictional retailer's website chat assistant told customers the wrong return window and some were refused at the returns desk. Contain it in the first hour, scope it from records, decide and cost the remedy, plan each message, run a blameless review, and set the conditions to turn auto-send back on.