Responsible AI Trade-offs Framework

Responsible AI is often treated as a compliance checklist after the model is chosen. Product teams need an explicit trade-off space earlier—when architecture and data contracts are still flexible.

A workshop-ready matrix linking use case severity to controls: human-in-the-loop defaults, grounding requirements, retention limits, geofencing, and escalation paths. Each control maps to owners and measurable operational metrics.

In validation with product and legal partners; used to gate agentic experiments before broad rollout.