Name the consequence.
Classify systems by what can happen to a person or organization when they are wrong, unavailable, abused, or misunderstood.
AI risk classification →A practical public-interest project
Responsible systems are operated, not declared.
Principles for teams building technology that can affect access, safety, privacy, livelihoods, or trust. Less ethics theater. More operational evidence.
Explore↓Preserve human authority.
High-consequence automation needs a clear owner who can inspect, override, stop, and explain the decision path.
Human oversight guide →Minimize the data boundary.
Collect and expose only what the job requires. Treat convenience as a reason to examine a data flow, not approve it.
Design for failure.
Test degraded behavior, rollback, manual continuity, and the evidence needed to reconstruct what happened.
Publish the operating truth.
Document limits, known risks, ownership, review cadence, and the conditions that would stop deployment.
Operating model
Turn responsible-AI principles into launch gates, ownership, evidence, and recovery.
Working session