Make AI risk a product requirement
Translate AI risk into mapped harms, measured signals, managed mitigations, and governed owners.
The reframe: risk is a product design input. Map the context AI risk depends on the actual use case: who uses the system, what data it touches, who is affected, and what happens when it fails. Mapping turns broad concern into concrete product constraints. Measure the failure modes Useful AI metrics are not only accuracy averages. They include groundedness, calibration, refusal behavior, subgroup performance, escalation rate, misuse attempts, and how often users override the output. Choose measures that match the harm. Manage with controls Controls can be interface choices, permission rules, approval gates, logging, monitoring, fallback paths, or scope limits.…
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